A response model can provide a significant boost to the efficiency of a marketing campaign by increasing responses or reducing expenses. The objective is to predict who will respond to an offer for a product or service
People
Products
Promotion
Place
O. Parr-Rud. Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner. SAS Institute, 2014.
The main objective is to train a predictive model which allows the company to maximize the profit of the next marketing campaign.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
# import dan backup raw dataset
raw_data = pd.read_csv('marketing_campaign.csv', sep=';')
data = raw_data.copy()
data.head()
| ID | Year_Birth | Education | Marital_Status | Income | Kidhome | Teenhome | Dt_Customer | Recency | MntWines | ... | NumWebVisitsMonth | AcceptedCmp3 | AcceptedCmp4 | AcceptedCmp5 | AcceptedCmp1 | AcceptedCmp2 | Complain | Z_CostContact | Z_Revenue | Response | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 5524 | 1957 | Graduation | Single | 58138.0 | 0 | 0 | 2012-09-04 | 58 | 635 | ... | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 11 | 1 |
| 1 | 2174 | 1954 | Graduation | Single | 46344.0 | 1 | 1 | 2014-03-08 | 38 | 11 | ... | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 11 | 0 |
| 2 | 4141 | 1965 | Graduation | Together | 71613.0 | 0 | 0 | 2013-08-21 | 26 | 426 | ... | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 11 | 0 |
| 3 | 6182 | 1984 | Graduation | Together | 26646.0 | 1 | 0 | 2014-02-10 | 26 | 11 | ... | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 11 | 0 |
| 4 | 5324 | 1981 | PhD | Married | 58293.0 | 1 | 0 | 2014-01-19 | 94 | 173 | ... | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 11 | 0 |
5 rows × 29 columns
# menampilkan informasi dataset
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2240 entries, 0 to 2239 Data columns (total 29 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ID 2240 non-null int64 1 Year_Birth 2240 non-null int64 2 Education 2240 non-null object 3 Marital_Status 2240 non-null object 4 Income 2216 non-null float64 5 Kidhome 2240 non-null int64 6 Teenhome 2240 non-null int64 7 Dt_Customer 2240 non-null object 8 Recency 2240 non-null int64 9 MntWines 2240 non-null int64 10 MntFruits 2240 non-null int64 11 MntMeatProducts 2240 non-null int64 12 MntFishProducts 2240 non-null int64 13 MntSweetProducts 2240 non-null int64 14 MntGoldProds 2240 non-null int64 15 NumDealsPurchases 2240 non-null int64 16 NumWebPurchases 2240 non-null int64 17 NumCatalogPurchases 2240 non-null int64 18 NumStorePurchases 2240 non-null int64 19 NumWebVisitsMonth 2240 non-null int64 20 AcceptedCmp3 2240 non-null int64 21 AcceptedCmp4 2240 non-null int64 22 AcceptedCmp5 2240 non-null int64 23 AcceptedCmp1 2240 non-null int64 24 AcceptedCmp2 2240 non-null int64 25 Complain 2240 non-null int64 26 Z_CostContact 2240 non-null int64 27 Z_Revenue 2240 non-null int64 28 Response 2240 non-null int64 dtypes: float64(1), int64(25), object(3) memory usage: 507.6+ KB
# total unique values setiap feature
data.nunique()
ID 2240 Year_Birth 59 Education 5 Marital_Status 8 Income 1974 Kidhome 3 Teenhome 3 Dt_Customer 663 Recency 100 MntWines 776 MntFruits 158 MntMeatProducts 558 MntFishProducts 182 MntSweetProducts 177 MntGoldProds 213 NumDealsPurchases 15 NumWebPurchases 15 NumCatalogPurchases 14 NumStorePurchases 14 NumWebVisitsMonth 16 AcceptedCmp3 2 AcceptedCmp4 2 AcceptedCmp5 2 AcceptedCmp1 2 AcceptedCmp2 2 Complain 2 Z_CostContact 1 Z_Revenue 1 Response 2 dtype: int64
# Z_CostContact adalah jumlah biaya yang digunakan di setiap kampanye, karena semua nilainya sama di semua pelanggan, maka tidak dapat digunakan dalam analisis
# Z_Revenue adalah jumlah pendapatan yang dihasilkan oleh setiap kampanye, karena semua nilainya sama di semua pelanggan, maka tidak dapat digunakan dalam analisis
# ID adalah CustomerID, karena semua pelanggan adalah unik, maka tidak dapat digunakan dalam analisis
data.drop(['ID', 'Z_CostContact', 'Z_Revenue'], axis=1, inplace=True)
# identifikasi missing values
pct_missing_values = data.isnull().sum().sum()/data.shape[0]*100
print(f'Total % Missing Values: {pct_missing_values:.2f}%')
Total % Missing Values: 1.07%
# identifikasi duplicated
pct_duplicates = data[data.duplicated()].shape[0]/data.shape[0]*100
print(f'Total % Duplicated Rows: {pct_duplicates:.2f}%')
Total % Duplicated Rows: 8.12%
# mengubah tipe data Dt_customer dari object menjadi datetime
data['Dt_Customer'] = pd.to_datetime(data['Dt_Customer'])
# memisahkan feature berdasarkan tipe data
numeric_cols = []
object_cols = []
datetime_cols = []
# melakukan perulangan setiap feature sesuai dengan tipe data
for col in data.columns:
if data[col].dtype == 'int64' or data[col].dtype == 'float64':
numeric_cols.append(col)
elif data[col].dtype == 'object':
object_cols.append(col)
else:
datetime_cols.append(col)
# print hasilnya yang disimpan pada masing-masing feature
print(f'Total Numeric Columns : {len(numeric_cols)}')
print(f'Total Object Columns : {len(object_cols)}')
print(f'Total Datetime Columns: {len(datetime_cols)}')
Total Numeric Columns : 23 Total Object Columns : 2 Total Datetime Columns: 1
# memisahkan feature berdasarkan klasifikasinya
personal_cols = []
spending_cols = []
purchase_cols = []
campaign_cols = []
# melakukan perulangan setiap feature sesuai dengan klasifikasinya
for col in data.columns:
if col in 'Year_Birth, Education, Marital_Status, Income, Kidhome, Teenhome, Dt_Customer, Recency, Complain':
personal_cols.append(col)
elif col in 'MntWines, MntFruits, MntMeatProducts, MntFishProducts, MntSweetProducts, MntGoldProds':
spending_cols.append(col)
elif col in 'NumDealsPurchases, NumWebPurchases, NumCatalogPurchases, NumStorePurchases, NumWebVisitsMonth':
purchase_cols.append(col)
elif col in 'AcceptedCmp1, AcceptedCmp2, AcceptedCmp3, AcceptedCmp4, AcceptedCmp5, Response, NumDealsPurchases':
campaign_cols.append(col)
# mengurutkan hasilnya sesuai dengan alphabet
personal_cols.sort()
spending_cols.sort()
purchase_cols.sort()
campaign_cols.sort()
# print hasilnya yang disimpan pada masing-masing feature
print(f'Personal Columns: {personal_cols}')
print(f'Spending Columns: {spending_cols}')
print(f'Purchase Columns: {purchase_cols}')
print(f'Campaign Columns: {campaign_cols}')
Personal Columns: ['Complain', 'Dt_Customer', 'Education', 'Income', 'Kidhome', 'Marital_Status', 'Recency', 'Teenhome', 'Year_Birth'] Spending Columns: ['MntFishProducts', 'MntFruits', 'MntGoldProds', 'MntMeatProducts', 'MntSweetProducts', 'MntWines'] Purchase Columns: ['NumCatalogPurchases', 'NumDealsPurchases', 'NumStorePurchases', 'NumWebPurchases', 'NumWebVisitsMonth'] Campaign Columns: ['AcceptedCmp1', 'AcceptedCmp2', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'Response']
# descriptive statistics pada feature bertipe data numerik
data[numeric_cols].describe(percentiles=list(np.linspace(0.1, 0.9,9))).T
| count | mean | std | min | 10% | 20% | 30% | 40% | 50% | 60% | 70% | 80% | 90% | max | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Year_Birth | 2240.0 | 1968.805804 | 11.984069 | 1893.0 | 1952.0 | 1957.0 | 1962.0 | 1966.0 | 1970.0 | 1973.0 | 1976.0 | 1979.0 | 1984.0 | 1996.0 |
| Income | 2216.0 | 52247.251354 | 25173.076661 | 1730.0 | 24117.5 | 32011.0 | 38198.5 | 44529.0 | 51381.5 | 58482.0 | 65247.5 | 71819.0 | 79844.0 | 666666.0 |
| Kidhome | 2240.0 | 0.444196 | 0.538398 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 |
| Teenhome | 2240.0 | 0.506250 | 0.544538 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 |
| Recency | 2240.0 | 49.109375 | 28.962453 | 0.0 | 9.0 | 19.0 | 29.0 | 39.0 | 49.0 | 59.0 | 69.0 | 79.0 | 89.0 | 99.0 |
| MntWines | 2240.0 | 303.935714 | 336.597393 | 0.0 | 6.0 | 16.0 | 34.0 | 81.0 | 173.5 | 284.4 | 418.6 | 581.2 | 822.1 | 1493.0 |
| MntFruits | 2240.0 | 26.302232 | 39.773434 | 0.0 | 0.0 | 1.0 | 2.0 | 4.0 | 8.0 | 15.0 | 25.0 | 44.0 | 83.0 | 199.0 |
| MntMeatProducts | 2240.0 | 166.950000 | 225.715373 | 0.0 | 7.0 | 12.0 | 20.0 | 35.0 | 67.0 | 108.4 | 177.0 | 298.4 | 499.0 | 1725.0 |
| MntFishProducts | 2240.0 | 37.525446 | 54.628979 | 0.0 | 0.0 | 2.0 | 3.0 | 7.0 | 12.0 | 20.0 | 37.0 | 65.0 | 120.0 | 259.0 |
| MntSweetProducts | 2240.0 | 27.062946 | 41.280498 | 0.0 | 0.0 | 1.0 | 2.0 | 5.0 | 8.0 | 14.0 | 26.0 | 44.2 | 89.0 | 263.0 |
| MntGoldProds | 2240.0 | 44.021875 | 52.167439 | 0.0 | 3.0 | 6.0 | 11.0 | 17.0 | 24.0 | 34.0 | 46.0 | 73.0 | 122.0 | 362.0 |
| NumDealsPurchases | 2240.0 | 2.325000 | 1.932238 | 0.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 3.0 | 3.0 | 5.0 | 15.0 |
| NumWebPurchases | 2240.0 | 4.084821 | 2.778714 | 0.0 | 1.0 | 2.0 | 2.0 | 3.0 | 4.0 | 4.0 | 5.0 | 6.0 | 8.0 | 27.0 |
| NumCatalogPurchases | 2240.0 | 2.662054 | 2.923101 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 2.0 | 2.0 | 4.0 | 5.0 | 7.0 | 28.0 |
| NumStorePurchases | 2240.0 | 5.790179 | 3.250958 | 0.0 | 2.0 | 3.0 | 3.0 | 4.0 | 5.0 | 6.0 | 7.0 | 9.0 | 11.0 | 13.0 |
| NumWebVisitsMonth | 2240.0 | 5.316518 | 2.426645 | 0.0 | 2.0 | 3.0 | 4.0 | 5.0 | 6.0 | 6.0 | 7.0 | 7.0 | 8.0 | 20.0 |
| AcceptedCmp3 | 2240.0 | 0.072768 | 0.259813 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| AcceptedCmp4 | 2240.0 | 0.074554 | 0.262728 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| AcceptedCmp5 | 2240.0 | 0.072768 | 0.259813 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| AcceptedCmp1 | 2240.0 | 0.064286 | 0.245316 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| AcceptedCmp2 | 2240.0 | 0.013393 | 0.114976 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| Complain | 2240.0 | 0.009375 | 0.096391 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| Response | 2240.0 | 0.149107 | 0.356274 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 |
# descriptive statistics pada feature bertipe data object/categorical
data[object_cols].describe().T
| count | unique | top | freq | |
|---|---|---|---|---|
| Education | 2240 | 5 | Graduation | 1127 |
| Marital_Status | 2240 | 8 | Married | 864 |
# descriptive statistics pada feature bertipe data datetime
data.describe(include='datetime').T
| count | unique | top | freq | first | last | |
|---|---|---|---|---|---|---|
| Dt_Customer | 2240 | 663 | 2012-08-31 | 12 | 2012-07-30 | 2014-06-29 |
Tipe Data Pada Dataset
integer atau float)object)Missing Values dan Duplicates
IncomeFeature dengan Tipe Data Tidak Sesuai:
Dt_Customer merupakan tanggal registrasi pelanggan dengan tipe data object. Perlu diubah menjadi tipe Date Time.Feature dengan Summary Aneh:
ID memiliki jumlah nilai unik yang sama dengan jumlah baris dataset (2240), sehingga tidak memungkinkan untuk mengamati riwayat perjalanan pelanggan.Z_CostContact dan Z_Revenue, masing-masing adalah cost dan revenue karena setiap campaign adalah unique maka tidak dapat digunakan dalam analisa.Dt_Customer, pelanggan paling terakhir melakukan registrasi di 29 Juni 2014, maka dengan asumsi saat ini adalah tahun 2014, ada keanehan pada Feature Year_Birth dimana tahun lahir tertua ada di tahun 1893 atau usia pelanggan 121 tahun. Hal ini merupakan hal yang kurang masuk akal. Diduga terdapat kesalahan input tahun lahir oleh pelanggan/kesalahan pencatatan oleh sistem.Income memiliki nilai maksimum mencapai ratusan ribu (666,666), sedangkan nilai ukuran pemusatan dan penyebarannya hanya mencapai puluhan ribu. Diduga nilai ini merupakan outlier yang disebabkan karena kesalahan input atau pencatatan oleh sistem.MntFishProducts, MntFruits, MntGoldProds, MntMeatProducts, MntSweetProducts, MntWines memiliki nilai maksimum yang jauh dari ukuran pemusatan atau penyebaran lainnya, menunjukkan adanya outlier, namun hal ini perlu dipertimbangkan kembali dari konteks bisnis apakah wajar atau tidaknya pembelian dalam jumlah tersebut.Marital Status memiliki 8 nilai unik dan Feature Education memiliki 5 nilai unik.Marital_Status seperti YOLO, Alone, dan Absurd. Dan pada Feature Education yaitu Basic dan 2n Cycle, apakah termasuk dalam level pendidikan formal atau bukan.plt.figure(figsize=(20, 10))
for i in range(0, len(numeric_cols)):
plt.subplot(5, 5, i + 1)
sns.kdeplot(data[numeric_cols[i]])
plt.tight_layout()
plt.show()
Recency memiliki distribusi yang mirip dengan distribusi normal.Year_Birth menunjukkan kecondongan ke kiri dengan median yang lebih tinggi daripada mean.Income dan beberapa features terkait pembelian produk menunjukkan kecondongan ke kanan, dengan mean yang lebih tinggi daripada median.Kidhome dan Teenhome menunjukkan dua puncak dalam distribusinya.AcceptedCmp1, AcceptedCmp2, AcceptedCmp3, AcceptedCmp4, AcceptedCmp5, dan Responsee) didominasi oleh nilai 0.plt.figure(figsize=(20, 10))
for i in range(0, len(numeric_cols[:-7])):
plt.subplot(4, 4, i+1)
sns.boxplot(y=data[numeric_cols[i]])
plt.tight_layout()
Year_Birth dan Income memiliki outlier seperti yang telah dijelaskan pada bagian sebelumnya, yakni pada angka 666.666 (Income) dan 1893 (Year_Birth).# setup ukuran figure
plt.figure(figsize=(12, 8))
# iterasi untuk setiap Feature objek
for i in range(0, len(object_cols)):
# mendapatkan urutan kategori berdasarkan jumlah masing-masing
order = raw_data[object_cols[i]].value_counts().index
# membuat subplot
plt.subplot(2, len(object_cols), i + 1)
# countplot dengan urutan kategori yang diinginkan
ax = sns.countplot(x=raw_data[object_cols[i]], order=order, palette=sns.color_palette())
# menambahkan label di atas setiap bar
for p in ax.patches:
ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()),
ha='center', va='center', xytext=(0, 5), textcoords='offset points')
# setup pada xticks
plt.xticks(rotation=0, ha='center')
plt.tight_layout()
plt.show()
Marital_Status dan Education.# exclude data yang tidak valid seperti Basic dan 2n Cycle pada Education dan Absurd, Alone, YOLO pada Marital Status
filter_data = (data['Education'].isin(['Graduation', 'Master', 'PhD'])) \
& (data['Marital_Status'].isin(['Single', 'Married', 'Divorced']))
# 'Education'
education_counts = data[filter_data].groupby(
'Education')['Response'].value_counts(normalize=True).unstack()
colors = ['#e41a1c', '#377eb8']
fig, axes = plt.subplots(1, 2, figsize=(12, 6))
education_counts.plot(kind='bar', stacked=True, color=colors, ax=axes[0])
axes[0].set_title('Percentage of Response by Education')
axes[0].set_ylabel('Percentage')
axes[0].set_xticklabels(education_counts.index, rotation=0, ha='center')
handles, labels = axes[0].get_legend_handles_labels()
axes[0].legend(reversed(handles), reversed(labels), bbox_to_anchor=(
1.2, 1), loc='upper right', title='Response')
for p in axes[0].patches:
if p.get_height() >= 0.5:
vertical_position = 'bottom' if p.get_height() < 0.5 else 'top'
axes[0].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., p.get_height()),
ha='center', va=vertical_position, xytext=(0, 10 if p.get_height() < 0.5 else -10),
fontsize=12, color='white',
textcoords='offset points')
for p in axes[0].patches:
if p.get_height() < 0.5:
axes[0].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., 1),
ha='center', va='top', xytext=(0, -1),
fontsize=12, color='white',
textcoords='offset points')
# 'Marital_Status'
marital_counts = data[filter_data].groupby(
'Marital_Status')['Response'].value_counts(normalize=True).unstack()
marital_counts.plot(kind='bar', stacked=True, color=colors, ax=axes[1])
axes[1].set_title('Percentage of Response by Marital_Status')
axes[1].set_ylabel('Percentage')
axes[1].set_xticklabels(marital_counts.index, rotation=0, ha='center')
handles, labels = axes[1].get_legend_handles_labels()
axes[1].legend(reversed(handles), reversed(labels), bbox_to_anchor=(
1.2, 1), loc='upper right', title='Response')
for p in axes[1].patches:
if p.get_height() >= 0.5:
vertical_position = 'bottom' if p.get_height() < 0.5 else 'top'
axes[1].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., p.get_height()),
ha='center', va=vertical_position, xytext=(0, 10 if p.get_height() < 0.5 else -10),
fontsize=12, color='white',
textcoords='offset points')
for p in axes[1].patches:
if p.get_height() <= 0.5:
axes[1].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., 1),
ha='center', va='top', xytext=(0, -1),
fontsize=12, color='white',
textcoords='offset points')
plt.tight_layout()
plt.show()
Tingkat Pendidikan:
Status Perkawinan:
# membuat function untuk menampilkan trend distribusi pelanggan yang melakukan registrasi/enrollment setiap bulannya
def monthly_distribution(data, date_col, figsize=(12, 5)):
# setup subplot untuk line plot
fig, ax = plt.subplots(figsize=figsize)
# resample data dan hitung jumlah bulanan
monthly_counts = data.resample('M', on=date_col).size()
# plot line distribusi datetime (per bulan)
ax.plot(monthly_counts.index, monthly_counts.values,
marker='o', linestyle='-')
ax.set_title(f'Customer Registration Distribution Trends')
ax.set_xlabel(date_col)
ax.set_ylabel('frekuensi')
# memutar label pada sumbu x agar lebih mudah dibaca
plt.xticks(rotation=45)
# membuat indeks bulanan dari Juli 2012 hingga Juni 2014
monthly_index = pd.date_range(
start='2012-07-01', end='2014-06-30', freq='M')
# melakukan reindex dengan nilai pada variabel monthly_index
monthly_counts = monthly_counts.reindex(monthly_index, fill_value=0)
# mengubah format xticklabels menjadi nama bulan-tahun
month_year_labels = [date.strftime('%b-%Y')
for date in monthly_counts.index]
ax.set_xticks(monthly_counts.index)
ax.set_xticklabels(month_year_labels)
# menampilkan plot
plt.tight_layout()
plt.show()
# memanggil function menampilkan trend pelanggan yang registrasi/enrollment
monthly_distribution(data, 'Dt_Customer')
# membuat function untuk menampilkan trend distribusi pelanggan yang melakukan registrasi/enrollment setiap bulannya bersadarkan response
def monthly_distribution_response(data, date_col, figsize=(12, 5)):
# setup subplot untuk line plot
fig, ax = plt.subplots(figsize=figsize)
# resample data dan hitung jumlah bulanan untuk response = 0
monthly_counts_0 = data[data['Response'] == 0].resample('M', on='Dt_Customer').size()
# plot line distribusi datetime (per bulan) untuk response = 0
ax.plot(monthly_counts_0.index, monthly_counts_0.values,
marker='o', linestyle='-', label='Response = 0', color = "#ff0000")
# resample data dan hitung jumlah bulanan untuk response = 1
monthly_counts_1 = data[data['Response'] == 1].resample('M', on='Dt_Customer').size()
# plot line distribusi datetime (per bulan) untuk response = 1
ax.plot(monthly_counts_1.index, monthly_counts_1.values,
marker='o', linestyle='-', label='Response = 1', color = "#438cc4")
# Set title, xlabel, dan ylabel
ax.set_title('Customer Registration Distribution Trends')
ax.set_xlabel('Dt_Customer')
ax.set_ylabel('Frekuensi')
# memutar label pada sumbu x agar lebih mudah dibaca
plt.xticks(rotation=45)
# membuat indeks bulanan dari Juli 2012 hingga Juni 2014
monthly_index = pd.date_range(
start='2012-07-01', end='2014-06-30', freq='M')
# melakukan reindex dengan nilai pada variabel monthly_index untuk response = 0
monthly_counts_0 = monthly_counts_0.reindex(monthly_index, fill_value=0)
# melakukan reindex dengan nilai pada variabel monthly_index untuk response = 1
monthly_counts_1 = monthly_counts_1.reindex(monthly_index, fill_value=0)
# mengubah format xticklabels menjadi nama bulan-tahun
month_year_labels = [date.strftime('%b-%Y')
for date in monthly_index]
ax.set_xticks(monthly_index)
ax.set_xticklabels(month_year_labels)
# Menambah legenda
ax.legend()
# menampilkan plot
plt.tight_layout()
plt.show()
# memanggil function menampilkan trend pelanggan yang registrasi/enrollment berdasarkan response
monthly_distribution_response(data, 'Dt_Customer')
# regplot setiap feature ke target
plt.figure(figsize=(10, 20))
plt.subplots_adjust(hspace=0.5, wspace=0.5)
for i, col in enumerate(data[numeric_cols[1:-1]]):
plt.subplot(7, 3, i+1)
sns.regplot(data=data, x=data[col], y=data['Response'], logistic=True)
plt.title(f'{col} vs Response')
plt.xlabel(f'{col}')
plt.ylabel('Response')
plt.tight_layout()
plt.show()
Income) mempunyai korelasi positif yang cukup kuat dimana semakin tinggi income maka probabilitas merespon campaign semakin tinggi. Contohnya saat Income berada pada angka 200000 mempunyai probabilitas response sekitar 60%.Teenhome, Kidhome) mempunyai korelasi negatif dimana jumlah anak akan menurunkan probabilitas meresponse campaign. Contohnya saat mempunyai 2 anak remaja (Teenhome) akan menurunkan probabilitas campaign sampai mendekati 0%, sedangkan saat tidak mempunyai anak remaja probabilitas mencapai 20%.MeatProducts, juga memiliki korelasi positif yang signifikan dengan Response. Semakin banyak produk yang dibeli maka probabilitas untuk meresponse campaign semakin tinggi.AcceptedCmp1, AcceptedCmp2, AcceptedCmp3, AcceptedCmp4, AcceptedCmp5) juga memiliki korelasi positif yang signifikan terhadap Response.# menghitung korelasi dengan Response
CORR_RESPONSE = data.corrwith(data['Response'], axis=0, method='pearson', drop=False).sort_values(ascending=False)
# setup figure dan color palette
cmap = sns.diverging_palette(10, 240, as_cmap=True)
plt.figure(figsize=(6, 6))
# heatmap response
sns.heatmap(CORR_RESPONSE.to_frame(), annot=True, fmt=".2f", cbar=True, center=0, cmap=cmap)
plt.title('Correlation to Response')
Text(0.5, 1.0, 'Correlation to Response')
# setup figure dan color palette
cmap = sns.diverging_palette(10, 240, as_cmap=True)
plt.figure(figsize=(12, 8))
# heatmap matrix correlation
sns.heatmap(data.corr(), annot=True, fmt=".2f", cbar=True, center=0, cmap=cmap)
plt.title('Matrix Correlation')
plt.tight_layout()
plt.show()
# menghitung jumlah responden dengan Yes Response tanpa menerima campaign manapun
no_campaign_response = len(data[(data['AcceptedCmp1'] == 0) & (data['AcceptedCmp2'] == 0) & (
data['AcceptedCmp3'] == 0) & (data['AcceptedCmp4'] == 0) & (data['AcceptedCmp5'] == 0) & (data['Response'] == 1)])
# menghitung jumlah responden dengan Yes Response
total_yes_response = len(data[data['Response'] == 1])
# menghitung jumlah responden dengan Yes Response yang setidaknya menerima satu campaign
at_least_one_campaign_response = total_yes_response - no_campaign_response
# menampilkan hasil
print('Total Yes Response:', total_yes_response)
print('Yes Response without accepting any campaigns:', no_campaign_response)
print('Yes Response accepting at least one campaign:',
at_least_one_campaign_response)
# menampilkan proporsi dalam persentase
percentage_no_campaign_response = (
no_campaign_response / total_yes_response) * 100
percentage_at_least_one_campaign_response = (
at_least_one_campaign_response / total_yes_response) * 100
print('Percentage of Yes Response without accepting any campaigns: {:.1f}%'.format(
percentage_no_campaign_response))
print('Percentage of Yes Response accepting at least one campaign: {:.1f}%'.format(
percentage_at_least_one_campaign_response))
Total Yes Response: 334 Yes Response without accepting any campaigns: 146 Yes Response accepting at least one campaign: 188 Percentage of Yes Response without accepting any campaigns: 43.7% Percentage of Yes Response accepting at least one campaign: 56.3%
# success rate untuk setiap campaign yang diberikan
# inisialisasi dataFrame
campaigns = ['Cmp1', 'Cmp2', 'Cmp3', 'Cmp4', 'Cmp5']
df_marketing = pd.DataFrame(
0, index=campaigns + ['Any campaigns', 'Total Response'], columns=['Count', 'Yes Response', 'Success Rate'])
# menghitung metrik untuk setiap campaign
for col in ['AcceptedCmp1', 'AcceptedCmp2', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5']:
count = data[col].sum()
yes_response = data[(data[col] == 1) & (data['Response'] == 1)].shape[0]
success_rate = np.round(yes_response / count * 100, 2)
# mengisi nilai DataFrame
df_marketing.loc[col[8:], ['Count', 'Yes Response', 'Success Rate']] = [
count, yes_response, success_rate]
# menghitung metrik untuk 'Any campaigns' dan 'Total Response'
df_marketing.loc['Any campaigns', 'Count'] = len(data) - 1777
df_marketing.loc['Any campaigns', 'Yes Response'] = len(
data[data['Response'] == 1])
df_marketing.loc['Any campaigns', 'Success Rate'] = np.round(
df_marketing.loc['Any campaigns', 'Yes Response'] / df_marketing.loc['Any campaigns', 'Count'] * 100, 2)
df_marketing.loc['Total Response', 'Count'] = len(data)
df_marketing.loc['Total Response', 'Yes Response'] = len(
data[data['Response'] == 1])
df_marketing.loc['Total Response', 'Success Rate'] = np.round(
df_marketing.loc['Total Response', 'Yes Response'] / len(data) * 100, 2)
# menampilkan dataFrame
df_marketing
| Count | Yes Response | Success Rate | |
|---|---|---|---|
| Cmp1 | 144 | 79 | 54.86 |
| Cmp2 | 30 | 20 | 66.67 |
| Cmp3 | 163 | 77 | 47.24 |
| Cmp4 | 167 | 62 | 37.13 |
| Cmp5 | 163 | 92 | 56.44 |
| Any campaigns | 463 | 334 | 72.14 |
| Total Response | 2240 | 334 | 14.91 |
# subplots
fig, axes = plt.subplots(2, 1, figsize=(7, 7))
ax = axes.ravel()
# pie chart
colors = ['#377eb8','#e41a1c']
labels = ['Accepting at least one campaign', 'Not accepting any campaign']
sizes = [percentage_at_least_one_campaign_response,
percentage_no_campaign_response]
ax[0].pie(sizes, labels=labels, autopct='%1.1f%%', colors=colors, startangle=90, textprops={'color': 'white'})
ax[0].set_title('Percentage of Yes Response with and without Accepting Campaigns')
# bar chart for Success Rate
ax[1].bar(df_marketing.index[:-2], df_marketing['Success Rate'][:-2], color='#377eb8', alpha=0.7, label='Success Rate')
ax[1].set_xlabel('Campaigns')
ax[1].set_ylabel('Success Rate (%)')
ax[1].set_title('Success Rate for Each Campaign')
# Menghilangkan garis x dan y
ax[1].spines['top'].set_visible(False)
ax[1].spines['right'].set_visible(False)
ax[1].spines['bottom'].set_visible(True)
ax[1].spines['left'].set_visible(True)
ax[1].tick_params(axis='both', which='both', length=0) # Menghilangkan tanda tick pada x dan y
# values pada bar chart
for i, v in enumerate(df_marketing['Success Rate'][:-2]):
ax[1].text(i, v + 1, str(v) + '%', ha='center', va='bottom', fontsize=8)
plt.tight_layout(h_pad=1)
plt.show()
# membuat function untuk menampilkan plot pada agregasi data
def plot_grouped(data, group_column, variables, nrows=1, ncols=1, method='mean', hue=None):
# melakukan filter pada feature yang bertipe data numerik
numeric_variables = [
var for var in variables if pd.api.types.is_numeric_dtype(data[var])]
# agregasi data berdasarkan hasil feature yang telah terfilter sebelumnya
grouped_data = data.groupby([group_column, hue]).agg(
{variable: method for variable in numeric_variables}).reset_index()
# setup color palette
palette = sns.color_palette('Set1', len(numeric_variables))
# setup subplots
fig, axes = plt.subplots(nrows=nrows, ncols=ncols,
figsize=(10 * ncols, 5 * nrows))
# untuk memastikan bahwa axes tetap iterable untuk kasus 1 subplot
if nrows == 1 and ncols == 1:
axes = [axes]
# agregasi sesuai dengan methode yang terdefinisi pada parameter
if method == 'mean':
title = 'Average'
elif method == 'sum':
title = 'Total'
elif method == 'count':
title = 'Count'
# melakukan perulangan pada setiap feature yang ditampilkan
for i, variable in enumerate(numeric_variables):
# menentukan posisi axes setiap feature
row_index = i // ncols
col_index = i % ncols
ax = sns.barplot(x=group_column, y=variable, hue=hue,
data=grouped_data, ax=axes[row_index][col_index], palette=palette)
# menentukan title, xlabel, ylabel, dan xticks
axes[row_index][col_index].set_title(
f'{title} {variable} by {group_column}')
axes[row_index][col_index].set_ylabel(variable)
axes[row_index][col_index].set_xlabel(group_column)
axes[row_index][col_index].tick_params(axis='x', rotation=0)
# menambahkan label angka di atas setiap bar
for p in ax.patches:
height = p.get_height()
axes[row_index][col_index].text(p.get_x() + p.get_width()/2., height,
f'{height:.2f}', ha='center', va='bottom', color='black', fontsize=10)
plt.tight_layout()
plt.show()
# memilih feature yang akan ditampilkan insightnya
cols_to_plot = ['Income', 'Recency',
'MntMeatProducts', 'MntWines']
# memanggil function untuk menampilkan barplot pada agregasi feature education
plot_grouped(data[filter_data], 'Education', cols_to_plot,
nrows=2, ncols=2, hue='Response')
# memanggil function untuk menampilkan barplot pada agregasi marital status
plot_grouped(data[filter_data], 'Marital_Status', cols_to_plot,
nrows=2, ncols=2, hue='Response')
Berdasarkan analisis mendalam terhadap data campaign dan karakteristik pelanggan, kami menyarankan beberapa langkah strategis untuk meningkatkan efektivitas campaign dan memaksimalkan keuntungan bisnis:
Segmentasi Pelanggan Berdasarkan Respons campaign:
Melakukan segmentasi pelanggan berdasarkan respons campaign dapat membantu dalam menyesuaikan strategi pemasaran. Fokuskan upaya pada kelompok pelanggan yang telah menunjukkan respons positif, seperti tingkat pendidikan Graduation, PhD, dan Master, serta status pernikahan Single, Married, dan Divorced.
Personalisasi Pesan dan Penawaran:
Personalisasi pesan dan penawaran campaign untuk setiap kelompok pelanggan yang telah diidentifikasi dapat meningkatkan keterlibatan. Berdasarkan karakteristik unik dari setiap kelompok, buatlah pesan yang relevan dan tawarkan insentif yang sesuai dengan preferensi mereka.
Penargetan Tingkat Pendidikan Tinggi:
Tingkat pendidikan tinggi seperti Graduation, PhD, dan Master memiliki potensi besar untuk respons campaign. Fokuskan penawaran khusus, informasi produk, dan keuntungan tambahan pada kelompok ini untuk memaksimalkan partisipasi.
Optimalkan Pengeluaran Pelanggan yang Merespon:
Pelanggan yang merespons campaign memiliki kecenderungan pengeluaran yang lebih tinggi pada berbagai kategori produk. Optimalisasi persediaan dan promosi pada produk-produk yang paling diminati oleh kelompok pelanggan ini dapat meningkatkan nilai transaksi.
Perkuat campaign dengan Data Pembelian dan Channel:
Analisis menunjukkan bahwa pelanggan yang merespons campaign memiliki rata-rata pembelian yang lebih tinggi di berbagai saluran seperti catalog, web, dan toko fisik. Penguatan campaign dengan peningkatan ketersediaan produk melalui saluran ini dapat meningkatkan aksesibilitas produk bagi pelanggan.
Pemantauan Terus-Menerus dan Analisis Reaksi Pelanggan:
Melakukan pemantauan terus-menerus terhadap respons pelanggan dan melakukan analisis lebih lanjut terhadap perubahan tren dan preferensi. Keterlibatan yang berkelanjutan dan penyesuaian cepat terhadap dinamika pasar dapat menjadi kunci kesuksesan jangka panjang.
Dengan menerapkan strategi ini, diharapkan perusahaan dapat meraih keberhasilan yang lebih besar dalam campaign pemasaran, meningkatkan loyalitas pelanggan, dan mengoptimalkan hasil bisnis secara keseluruhan.
# melakukan backup dataset sebelum split
data_before_splitting = data.copy()
# restore point
data = data_before_splitting.copy()
data.head()
| Year_Birth | Education | Marital_Status | Income | Kidhome | Teenhome | Dt_Customer | Recency | MntWines | MntFruits | ... | NumCatalogPurchases | NumStorePurchases | NumWebVisitsMonth | AcceptedCmp3 | AcceptedCmp4 | AcceptedCmp5 | AcceptedCmp1 | AcceptedCmp2 | Complain | Response | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1957 | Graduation | Single | 58138.0 | 0 | 0 | 2012-09-04 | 58 | 635 | 88 | ... | 10 | 4 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 1 | 1954 | Graduation | Single | 46344.0 | 1 | 1 | 2014-03-08 | 38 | 11 | 1 | ... | 1 | 2 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 1965 | Graduation | Together | 71613.0 | 0 | 0 | 2013-08-21 | 26 | 426 | 49 | ... | 2 | 10 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 1984 | Graduation | Together | 26646.0 | 1 | 0 | 2014-02-10 | 26 | 11 | 4 | ... | 0 | 4 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 1981 | PhD | Married | 58293.0 | 1 | 0 | 2014-01-19 | 94 | 173 | 43 | ... | 3 | 6 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 rows × 26 columns
# split train and test set
# memisahkan antara training dan test set
from sklearn.model_selection import train_test_split
data_train, data_test = train_test_split(data, test_size=0.2, random_state=42)
# menampilkan shape dari train dan test set
print(f'data_train: {data_train.shape}, data_test: {data_test.shape}')
data_train: (1792, 26), data_test: (448, 26)
# backup dataset sebelum handling missing values
data_before_handling_missingvalues_train = data_train.copy()
data_before_handling_missingvalues_test = data_test.copy()
# restore point data_train
data_train = data_before_handling_missingvalues_train.copy()
data.head()
| Year_Birth | Education | Marital_Status | Income | Kidhome | Teenhome | Dt_Customer | Recency | MntWines | MntFruits | ... | NumCatalogPurchases | NumStorePurchases | NumWebVisitsMonth | AcceptedCmp3 | AcceptedCmp4 | AcceptedCmp5 | AcceptedCmp1 | AcceptedCmp2 | Complain | Response | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1957 | Graduation | Single | 58138.0 | 0 | 0 | 2012-09-04 | 58 | 635 | 88 | ... | 10 | 4 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 1 | 1954 | Graduation | Single | 46344.0 | 1 | 1 | 2014-03-08 | 38 | 11 | 1 | ... | 1 | 2 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 1965 | Graduation | Together | 71613.0 | 0 | 0 | 2013-08-21 | 26 | 426 | 49 | ... | 2 | 10 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 1984 | Graduation | Together | 26646.0 | 1 | 0 | 2014-02-10 | 26 | 11 | 4 | ... | 0 | 4 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 1981 | PhD | Married | 58293.0 | 1 | 0 | 2014-01-19 | 94 | 173 | 43 | ... | 3 | 6 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 rows × 26 columns
# restore point data_test
data_test = data_before_handling_missingvalues_test.copy()
data.head()
| Year_Birth | Education | Marital_Status | Income | Kidhome | Teenhome | Dt_Customer | Recency | MntWines | MntFruits | ... | NumCatalogPurchases | NumStorePurchases | NumWebVisitsMonth | AcceptedCmp3 | AcceptedCmp4 | AcceptedCmp5 | AcceptedCmp1 | AcceptedCmp2 | Complain | Response | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1957 | Graduation | Single | 58138.0 | 0 | 0 | 2012-09-04 | 58 | 635 | 88 | ... | 10 | 4 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 1 | 1954 | Graduation | Single | 46344.0 | 1 | 1 | 2014-03-08 | 38 | 11 | 1 | ... | 1 | 2 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 1965 | Graduation | Together | 71613.0 | 0 | 0 | 2013-08-21 | 26 | 426 | 49 | ... | 2 | 10 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 1984 | Graduation | Together | 26646.0 | 1 | 0 | 2014-02-10 | 26 | 11 | 4 | ... | 0 | 4 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 1981 | PhD | Married | 58293.0 | 1 | 0 | 2014-01-19 | 94 | 173 | 43 | ... | 3 | 6 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 rows × 26 columns
# membuat function untuk mengidentifikasi missing values
def identify_missing_values(data):
# menghitung missing values pada setiap feature
missing_values_count = data.isnull().sum().reset_index().rename(
{'index': 'column', 0: 'missing values'}, axis=1)
# filter feature yang mempunyai missing values
missing_values_count = missing_values_count[missing_values_count['missing values'] > 0]
# menghitng persentase missing values
missing_values_count['percentage'] = round(
(missing_values_count['missing values'] / len(data) * 100), 2)
# mengurutkan jumlah missing values secara descending
missing_values_count = missing_values_count.sort_values(
by='missing values', ascending=False).reset_index(drop=True)
return missing_values_count
# menampilkan data shape dan memanggil function untuk mengidentifikasi missing values
print(f'Data Train Shape : {data_train.shape}')
print(f'Data Test Shape : {data_test.shape}')
print(identify_missing_values(data_train))
print(identify_missing_values(data_test))
Data Train Shape : (1792, 26) Data Test Shape : (448, 26) column missing values percentage 0 Income 19 1.06 column missing values percentage 0 Income 5 1.12
# handling missing values, dengan menghapusnya (<10%)
data_train.dropna(axis=0, inplace=True)
data_test.dropna(axis=0, inplace=True)
print(data_train.shape)
print(data_test.shape)
(1773, 26) (443, 26)
# melakukan backup dataset sebelum handling duplicates
data_before_handling_duplicates_train = data_train.copy()
data_before_handling_duplicates_test = data_test.copy()
# restore point
data_train = data_before_handling_duplicates_train.copy()
data_test = data_before_handling_duplicates_test.copy()
# membuat function untuk mengidentifikasi data terduplikasi
def identify_duplicates(df):
# mencari baris yang terduplikasi
duplicate_rows = df[df.duplicated()]
# menghitng jumlah row yang terduplikasi
duplicate_count = len(duplicate_rows)
# membuat dataframe untuk menampilkan jumlah row beserta persentase data yang terduplikasi
if duplicate_count > 0:
duplicate_info = pd.DataFrame({
'duplicated rows': [duplicate_count],
'percentage': round((duplicate_count / len(df) * 100), 2)
})
else:
duplicate_info = pd.DataFrame({
'duplicated rows': [0],
'percentage': [0.0]
})
return duplicate_info
# menampikan data shape dan memanggil function untuk mengidentikasi data yang terduplikasi
print(f'Data Train Shape : {data_train.shape}')
print(f'Data Test Shape : {data_test.shape}')
print(identify_duplicates(data_train))
print(identify_duplicates(data_test))
Data Train Shape : (1773, 26) Data Test Shape : (443, 26) duplicated rows percentage 0 116 6.54 duplicated rows percentage 0 6 1.35
# menampilkan baris data yang terduplikasi pada data_train
data_train[data_train.duplicated(keep=False)].sort_values(by=['Education','Marital_Status','Income'])
| Year_Birth | Education | Marital_Status | Income | Kidhome | Teenhome | Dt_Customer | Recency | MntWines | MntFruits | ... | NumCatalogPurchases | NumStorePurchases | NumWebVisitsMonth | AcceptedCmp3 | AcceptedCmp4 | AcceptedCmp5 | AcceptedCmp1 | AcceptedCmp2 | Complain | Response | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1107 | 1970 | 2n Cycle | Married | 15315.0 | 0 | 0 | 2013-08-03 | 27 | 7 | 4 | ... | 0 | 4 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 558 | 1970 | 2n Cycle | Married | 15315.0 | 0 | 0 | 2013-08-03 | 27 | 7 | 4 | ... | 0 | 4 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1549 | 1975 | 2n Cycle | Married | 37284.0 | 1 | 1 | 2013-03-29 | 46 | 11 | 1 | ... | 0 | 3 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2015 | 1975 | 2n Cycle | Married | 37284.0 | 1 | 1 | 2013-03-29 | 46 | 11 | 1 | ... | 0 | 3 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 669 | 1971 | 2n Cycle | Married | 54690.0 | 1 | 1 | 2013-11-07 | 76 | 111 | 16 | ... | 1 | 5 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2062 | 1982 | PhD | Together | 70038.0 | 0 | 0 | 2013-10-25 | 54 | 587 | 54 | ... | 4 | 8 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1630 | 1947 | PhD | Together | 73059.0 | 0 | 1 | 2013-08-31 | 36 | 410 | 112 | ... | 3 | 13 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 658 | 1947 | PhD | Together | 73059.0 | 0 | 1 | 2013-08-31 | 36 | 410 | 112 | ... | 3 | 13 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1649 | 1950 | PhD | Widow | 56551.0 | 1 | 1 | 2014-05-07 | 48 | 67 | 4 | ... | 1 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1959 | 1950 | PhD | Widow | 56551.0 | 1 | 1 | 2014-05-07 | 48 | 67 | 4 | ... | 1 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
226 rows × 26 columns
# menampilkan baris data yang terduplikasi pada data_test
data_test[data_test.duplicated(keep=False)].sort_values(by=['Education','Marital_Status','Income'])
| Year_Birth | Education | Marital_Status | Income | Kidhome | Teenhome | Dt_Customer | Recency | MntWines | MntFruits | ... | NumCatalogPurchases | NumStorePurchases | NumWebVisitsMonth | AcceptedCmp3 | AcceptedCmp4 | AcceptedCmp5 | AcceptedCmp1 | AcceptedCmp2 | Complain | Response | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 508 | 1992 | Graduation | Married | 34935.0 | 0 | 0 | 2013-06-21 | 71 | 34 | 4 | ... | 1 | 4 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1578 | 1992 | Graduation | Married | 34935.0 | 0 | 0 | 2013-06-21 | 71 | 34 | 4 | ... | 1 | 4 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1318 | 1972 | Graduation | Married | 40321.0 | 1 | 1 | 2013-07-29 | 59 | 44 | 4 | ... | 0 | 3 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 316 | 1972 | Graduation | Married | 40321.0 | 1 | 1 | 2013-07-29 | 59 | 44 | 4 | ... | 0 | 3 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 705 | 1986 | Graduation | Married | 71952.0 | 1 | 0 | 2013-01-10 | 93 | 656 | 80 | ... | 4 | 8 | 4 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| 351 | 1986 | Graduation | Married | 71952.0 | 1 | 0 | 2013-01-10 | 93 | 656 | 80 | ... | 4 | 8 | 4 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| 1822 | 1971 | Master | Single | 33316.0 | 1 | 1 | 2013-10-04 | 34 | 79 | 1 | ... | 1 | 4 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1359 | 1971 | Master | Single | 33316.0 | 1 | 1 | 2013-10-04 | 34 | 79 | 1 | ... | 1 | 4 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1545 | 1986 | Master | Together | 42386.0 | 1 | 0 | 2013-01-13 | 43 | 65 | 4 | ... | 0 | 3 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 297 | 1986 | Master | Together | 42386.0 | 1 | 0 | 2013-01-13 | 43 | 65 | 4 | ... | 0 | 3 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1347 | 1983 | PhD | Married | 50150.0 | 0 | 0 | 2013-06-20 | 32 | 135 | 46 | ... | 2 | 7 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 99 | 1983 | PhD | Married | 50150.0 | 0 | 0 | 2013-06-20 | 32 | 135 | 46 | ... | 2 | 7 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
12 rows × 26 columns
# handling data yang terduplikasi dengan cara menghapusnya
data_train.drop_duplicates(keep='first', inplace=True)
data_test.drop_duplicates(keep='first', inplace=True)
print(f'Data Train Shape : {data_train.shape}')
print(f'Data Test Shape : {data_test.shape}')
Data Train Shape : (1657, 26) Data Test Shape : (437, 26)
# melakukan backup dataset sebelum handling outliers
data_before_handling_outliers_train = data_train.copy()
data_before_handling_outliers_test = data_test.copy()
# restore point
data_train = data_before_handling_outliers_train.copy()
data_test = data_before_handling_outliers_test.copy()
# z-score outliers handling
from scipy.stats import zscore
def handle_outliers_zscore(data, columns, threshold=3, plot=False):
filtered_entries = np.array([True] * len(data))
outliers_entries = np.array([True] * len(data))
for column in columns:
z_scores = np.abs(zscore(data[column]))
filtered = (z_scores < threshold)
filtered_entries = np.logical_and(filtered, filtered_entries)
outliers_entries = ~filtered_entries
filtered_data = data[filtered_entries]
outliers_data = data[outliers_entries]
filtered_data.reset_index(drop=True, inplace=True)
outliers_data.reset_index(drop=True, inplace=True)
if plot:
for column in columns:
fig, ax = plt.subplots(1, 2, figsize=(8, 4))
ax[0].set_title(f'Before Outliers in {column}')
sns.boxplot(data[column], ax=ax[0])
ax[1].set_title(f'After Outliers in {column}')
sns.boxplot(filtered_data[column], ax=ax[1])
plt.tight_layout()
plt.show()
data_size = data.shape[0]
filtered_size = filtered_data.shape[0]
outliers_size = outliers_data.shape[0]
pct_outliers = round((outliers_size / data_size) * 100, 2)
result = pd.Series(data=[data_size, filtered_size, outliers_size, pct_outliers],
index=['Before', 'After', 'Outliers', '% Outliers'])
print(result, '\n')
return filtered_data, outliers_data
# menghapus outlier pada feature income data_train
outlier_cols = ['Income']
data_train, residu_income_train = handle_outliers_zscore(data_train, outlier_cols, 3, True)
# menghapus outlier pada feature Year_Birth data_train
outlier_cols = ['Year_Birth']
data_train, residu_yearbirth_train = handle_outliers_zscore(data_train, outlier_cols, 3, True)
Before 1657.00 After 1650.00 Outliers 7.00 % Outliers 0.42 dtype: float64
Before 1650.00 After 1649.00 Outliers 1.00 % Outliers 0.06 dtype: float64
residu_income_train.sort_values(by='Income', ascending=False)
| Year_Birth | Education | Marital_Status | Income | Kidhome | Teenhome | Dt_Customer | Recency | MntWines | MntFruits | ... | NumCatalogPurchases | NumStorePurchases | NumWebVisitsMonth | AcceptedCmp3 | AcceptedCmp4 | AcceptedCmp5 | AcceptedCmp1 | AcceptedCmp2 | Complain | Response | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1977 | Graduation | Together | 666666.0 | 1 | 0 | 2013-06-02 | 23 | 9 | 14 | ... | 1 | 3 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | 1982 | PhD | Married | 160803.0 | 0 | 0 | 2012-08-04 | 21 | 55 | 16 | ... | 28 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | 1971 | Master | Together | 157733.0 | 1 | 0 | 2013-06-04 | 37 | 39 | 1 | ... | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 1973 | PhD | Married | 157243.0 | 0 | 1 | 2014-03-01 | 98 | 20 | 2 | ... | 22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 1977 | Graduation | Together | 157146.0 | 0 | 0 | 2013-04-29 | 13 | 1 | 0 | ... | 28 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 1949 | PhD | Married | 156924.0 | 0 | 0 | 2013-08-29 | 85 | 2 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 1975 | Graduation | Divorced | 153924.0 | 0 | 0 | 2014-02-07 | 81 | 1 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
7 rows × 26 columns
residu_yearbirth_train.sort_values(by='Year_Birth', ascending=False)
| Year_Birth | Education | Marital_Status | Income | Kidhome | Teenhome | Dt_Customer | Recency | MntWines | MntFruits | ... | NumCatalogPurchases | NumStorePurchases | NumWebVisitsMonth | AcceptedCmp3 | AcceptedCmp4 | AcceptedCmp5 | AcceptedCmp1 | AcceptedCmp2 | Complain | Response | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1899 | PhD | Together | 83532.0 | 0 | 0 | 2013-09-26 | 36 | 755 | 144 | ... | 6 | 4 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
1 rows × 26 columns
# handling invalid values
# mengubah value pada feature education
data_train['Education'].replace({'Basic':'Graduation','2n Cycle':'Master'}, inplace=True)
data_test['Education'].replace({'Basic':'Graduation','2n Cycle':'Master'}, inplace=True)
# mengubah value pada feature marital_status
data_train['Marital_Status'].replace({'YOLO':'Single','Absurd':'Single','Alone':'Single',
'Widow':'Divorced','Together':'Married'}, inplace=True)
data_train['Marital_Status'].replace({'YOLO':'Single','Absurd':'Single','Alone':'Single',
'Widow':'Divorced','Together':'Married'}, inplace=True)
# backup dataset sebelum melakukan feature extraction
data_before_extraction_train = data_train.copy()
data_before_extraction_test = data_test.copy()
data_before_extraction = data.copy()
# restore point
data_train = data_before_extraction_train.copy()
data_test = data_before_extraction_test.copy()
data = data_before_extraction.copy()
data_train['feature'] = 'train'
data_test['feature'] = 'test'
data = pd.concat([data_train,data_test]).reset_index(drop=True)
# membuat feature baru berdasarkan status hubungan
marital = {
'Single': 'Not in relationship',
'Together': 'In relationship',
'Married': 'In relationship',
'Divorced': 'Not in relationship',
'Widow': 'Not in relationship',
'Alone': 'Not in relationship',
'Absurd': 'Not in relationship',
'YOLO': 'Not in relationship'
}
data['Relationship_Status'] = data['Marital_Status'].map(marital)
# membuat feature baru total_children dari penjumlahan feature kidhome dan teenhome
data['Total_Children'] = data['Kidhome'] + data['Teenhome']
# membuat feature baru berdasarkan jumlah anggota keluarga
def fam_size(x):
if x['Relationship_Status'] == 'Not in relationship':
result = 1 + x['Teenhome'] + x['Kidhome']
elif x['Relationship_Status'] == 'In relationship':
result = 2 + x['Teenhome'] + x['Kidhome']
return result
data['Family_Size'] = data.apply(fam_size, axis=1)
# membuat feature baru berdasarkan tanggal bergabung dan diasumsikan data dikumpulkan pada awal juli 2014
data['Customer_Lifespan'] = (pd.to_datetime('2014-07-01') - data['Dt_Customer']).dt.days
# ekstraksi feature Datetime menjadi feature baru
data['Year'] = data['Dt_Customer'].dt.year
data.drop(['Dt_Customer'],axis=1,inplace=True)
# membuat feature baru total purchase, total spending, dan total offers
data['Total_Purchase'] = data.apply(lambda x: x[purchase_cols[:-1]].sum(), axis=1)
data['Total_Spending'] = data.apply(lambda x: x[spending_cols].sum(), axis=1)
data['Total_Offers'] = data.apply(lambda x: x[campaign_cols[:-1]].sum(), axis=1)
# membuat feature baru untuk menghitung ratio spending dengan total pembelian, dan ratio pembelian diskon dengan total pembelian
data['Spending_Purchase_Ratio'] = data['Total_Spending']/data['Total_Purchase']
data['Deal_Purchase_Ratio'] = data['NumDealsPurchases']/data['Total_Purchase']
# membuat feature baru accept one campaign and more than one campaign
data['at_least_one_campaign'] = data.apply(lambda x: 1 if x[campaign_cols[:-1]].sum() == 1 else 0, axis=1)
data['more_one_campaign'] = data.apply(lambda x: 1 if x[campaign_cols[:-1]].sum() > 1 else 0, axis=1)
# membuat feature baru untuk primary_needs
def categorize_needs(row):
primary_needs = row['MntFruits'] + row['MntMeatProducts'] + row['MntFishProducts']
secondary_needs = row['MntWines'] + row['MntGoldProds'] + row['MntSweetProducts']
if primary_needs > secondary_needs:
return 'primary_needs'
else:
return 'secondary_needs'
data['primary_needs'] = data.apply(categorize_needs, axis=1)
# identifikasi dan handling inf value dari 'Spending_Purchase_Ratio' dan 'Deal_Purchase_Ratio'
data.replace([np.inf, -np.inf], np.nan, inplace=True)
data.dropna(axis=0, inplace=True)
data.reset_index(drop=True, inplace=True)
data.head()
| Year_Birth | Education | Marital_Status | Income | Kidhome | Teenhome | Recency | MntWines | MntFruits | MntMeatProducts | ... | Customer_Lifespan | Year | Total_Purchase | Total_Spending | Total_Offers | Spending_Purchase_Ratio | Deal_Purchase_Ratio | at_least_one_campaign | more_one_campaign | primary_needs | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1950 | Graduation | Single | 16813.0 | 0 | 0 | 49 | 4 | 8 | 11 | ... | 347 | 2013 | 6 | 50 | 1 | 8.333333 | 0.166667 | 1 | 0 | primary_needs |
| 1 | 1963 | Master | Single | 64191.0 | 0 | 1 | 30 | 420 | 15 | 186 | ... | 525 | 2013 | 24 | 825 | 0 | 34.375000 | 0.125000 | 0 | 0 | secondary_needs |
| 2 | 1971 | PhD | Married | 71969.0 | 0 | 1 | 59 | 1000 | 0 | 76 | ... | 623 | 2012 | 19 | 1086 | 1 | 57.157895 | 0.157895 | 1 | 0 | secondary_needs |
| 3 | 1968 | PhD | Married | 29187.0 | 1 | 0 | 43 | 26 | 0 | 6 | ... | 418 | 2013 | 5 | 34 | 0 | 6.800000 | 0.200000 | 0 | 0 | secondary_needs |
| 4 | 1969 | Graduation | Married | 4428.0 | 0 | 1 | 0 | 16 | 4 | 12 | ... | 269 | 2013 | 25 | 359 | 0 | 14.360000 | 0.000000 | 0 | 0 | secondary_needs |
5 rows × 39 columns
# rfm encode
'''
Champions: Pelanggan dengan RFM score tertinggi.
Loyal Customers: Pelanggan dengan frekuensi tinggi dan nilai monetary tinggi.
At-Risk Customers: Pelanggan dengan recency rendah dan frekuensi rendah.
New Customers: Pelanggan dengan recency tinggi dan frekuensi rendah.
'''
# membuat feature baru category rfm score (champions, loyal, at risk, new)
rfm = pd.DataFrame()
rfm['Recency'] = data['Recency']
rfm['Frequency'] = data['Total_Purchase']
rfm['Monetary'] = data['Total_Spending']
rfm.head()
| Recency | Frequency | Monetary | |
|---|---|---|---|
| 0 | 49 | 6 | 50 |
| 1 | 30 | 24 | 825 |
| 2 | 59 | 19 | 1086 |
| 3 | 43 | 5 | 34 |
| 4 | 0 | 25 | 359 |
# kalkulasi score berdasarkan quantile masing-masing feature
rfm['recency_score'] = pd.qcut(rfm['Recency'], q=[0, 0.25, 0.5, 0.75, 1], labels=[4, 3, 2, 1])
rfm['frequency_score'] = pd.qcut(rfm['Frequency'], q=[0, 0.25, 0.5, 0.75, 1], labels=[1, 2, 3, 4])
rfm['monetary_score'] = pd.qcut(rfm['Monetary'], q=[0, 0.25, 0.5, 0.75, 1], labels=[1, 2, 3, 4])
# histplot rfm features
fig, axes = plt.subplots(1, 3, figsize=(12, 4))
sns.histplot(rfm['recency_score'], kde=True, ax=axes[0], color='#e9c369')
axes[0].set_title('Recency Score')
sns.histplot(rfm['frequency_score'], kde=True, ax=axes[1], color='#e9c369')
axes[1].set_title('Frequency Score')
sns.histplot(rfm['monetary_score'], kde=True, ax=axes[2], color='#e9c369')
axes[2].set_title('Monetary Score')
plt.tight_layout()
plt.show()
# histplot rfm score
rfm['rfm_score'] = rfm['recency_score'].astype('int') + rfm['frequency_score'].astype('int') + rfm['monetary_score'].astype('int')
sns.histplot(rfm['rfm_score'], kde=True, bins=10, color='#e9c369')
<Axes: xlabel='rfm_score', ylabel='Count'>
'''scoring rfm
0-4 new cust
5-7 at risk cust
8-10 loyal cust
10-12 champions
'''
rfm['RFM_Cat'] = pd.cut(rfm['rfm_score'], bins=[0,4,7,10,12], labels=['new cust','at risk cust','loyal cust','champions'])
ax = sns.countplot(x=rfm['RFM_Cat'], palette=sns.color_palette())
for p in ax.patches:
ax.annotate(f'{p.get_height():.0f}', (p.get_x() + p.get_width() / 2., p.get_height()),
ha='center', va='center', xytext=(0, 5), textcoords='offset points')
# concat dataframe data dengan rfm
data = pd.concat([data, rfm['RFM_Cat']], axis=1)
# backup dataset sebelum melakukan feature enconding
data_before_encoding = data.copy()
data_before_encoding.to_csv('data_before_encoding.csv', index=False)
# restore point
data = data_before_encoding.copy()
from sklearn.preprocessing import OrdinalEncoder
# encoding education
edu = {'Graduation': 1, 'Master': 2, 'PhD': 3}
oe_edu = OrdinalEncoder(categories=[list(edu.keys())])
data['Education'] = oe_edu.fit_transform(data[['Education']])
# encoding marital_status
marital_mapping = {'Single': 1, 'Married': 2, 'Divorced': 3}
data['Marital_Status'] = data['Marital_Status'].map(marital_mapping)
# encoding relationship_status
rel_mapping = {'Not in relationship': 0,
'In relationship': 1}
data['Relationship_Status'] = data['Relationship_Status'].map(
rel_mapping)
# encoding primary_needs
pr_mapping = {'primary_needs': 0,
'secondary_needs': 1}
data['primary_needs'] = data['primary_needs'].map(pr_mapping)
# encoding rfm_cat
rfm_mapping = {'new cust': 1, 'at risk cust': 2,
'loyal cust': 3, 'champions': 4}
oe_rfm = OrdinalEncoder(categories=[list(rfm_mapping.keys())])
data['RFM_Cat'] = oe_rfm.fit_transform(data[['RFM_Cat']])
# splitting dataset / restore data_train dan data_test
data_train = data[data['feature'] == 'train'].copy()
data_test = data[data['feature'] == 'test'].copy()
data_train.drop('feature', axis=1, inplace=True)
data_test.drop('feature', axis=1, inplace=True)
data_train.reset_index(drop=True, inplace=True)
data_test.reset_index(drop=True, inplace=True)
# backup data sebelum transformation
data_before_transform_train = data_train.copy()
data_before_transform_test = data_test.copy()
# restore point data train dan data test
data_train = data_before_transform_train.copy()
data_test = data_before_transform_test.copy()
from scipy.stats import skew, kurtosis
def assess_distribution_multi(data, features, alpha=0.05, show_plot=False):
num_features = len(features)
num_cols = min(4, num_features) # Maksimal 4 Feature dalam satu baris
num_rows = (num_features - 1) // num_cols + 1 # Jumlah baris yang dibutuhkan
if show_plot:
fig, axes = plt.subplots(num_rows, num_cols, figsize=(16, 4 * num_rows))
axes = axes.flatten()
skew_type_list = [] # Tambahkan inisialisasi list
skew_val_list = [] # Tambahkan inisialisasi list
normalization_advice_list = [] # Tambahkan inisialisasi list
transform_columns = [] # Tambahkan inisialisasi list
results = []
for i, feature_name in enumerate(features):
feature_data = data[feature_name].dropna(axis=0)
skew_val = round(skew(feature_data, nan_policy="omit"), 3)
if (len(feature_data) > 0): # Tambahkan kondisi untuk memastikan ada data sebelum melanjutkan
mean = round(feature_data.mean(), 3)
median = feature_data.median()
mode = feature_data.mode()[0]
q1 = feature_data.quantile(q=0.25)
q3 = feature_data.quantile(q=0.75)
if (mean == median == mode) or (-0.2 < skew_val < 0.2):
skew_type = "Normal Distribution (Symmetric)"
normalization_advice = "No Transformation"
elif mean < median < mode:
skew_type = "Negatively Skewed"
normalization_advice = "MinMaxScaler"
if skew_val <= -1:
skew_type = "Highly Negatively Skewed"
normalization_advice = "PowerTransformer/Log"
else:
skew_type = "Positively Skewed"
normalization_advice = "MinMaxScaler"
if skew_val >= 1:
skew_type = "Highly Positively Skewed"
normalization_advice = "PowerTransformer/Log"
skew_type_list.append(skew_type)
skew_val_list.append(skew_val)
normalization_advice_list.append(normalization_advice)
# Tambahkan fitur ke dalam daftar yang perlu ditransformasi atau standarisasi
if normalization_advice != "No Transformation":
transform_columns.append(feature_name)
# Gambar histplot dalam subplot
if show_plot:
sns.histplot(feature_data, kde=True, ax=axes[i])
axes[i].set_title(f'Histogram for {feature_name}')
if show_plot:
# Sembunyikan subplot yang tidak terpakai
for j in range(i + 1, len(axes)):
axes[j].axis('off')
plt.tight_layout()
plt.show()
# Buat DataFrame hasil penilaian distribusi
result_df = pd.DataFrame({
'Feature': features,
'Skewness': skew_val_list,
'Type of Distribution': skew_type_list,
'Normalization Advice': normalization_advice_list
}).set_index('Feature')
return result_df, transform_columns
# memilih feature yang akan ditransformasikan
cols_to_transform = data_train.drop('Response', axis=1).columns
cols_to_transform = sorted(cols_to_transform)
# identifikasi feature sebelum ditransformasi
result_table = assess_distribution_multi(data_train, cols_to_transform, show_plot=True)[0]
result_table
| Skewness | Type of Distribution | Normalization Advice | |
|---|---|---|---|
| Feature | |||
| AcceptedCmp1 | 3.531 | Highly Positively Skewed | PowerTransformer/Log |
| AcceptedCmp2 | 8.911 | Highly Positively Skewed | PowerTransformer/Log |
| AcceptedCmp3 | 3.188 | Highly Positively Skewed | PowerTransformer/Log |
| AcceptedCmp4 | 3.341 | Highly Positively Skewed | PowerTransformer/Log |
| AcceptedCmp5 | 3.254 | Highly Positively Skewed | PowerTransformer/Log |
| Complain | 9.151 | Highly Positively Skewed | PowerTransformer/Log |
| Customer_Lifespan | -0.011 | Normal Distribution (Symmetric) | No Transformation |
| Deal_Purchase_Ratio | 0.831 | Positively Skewed | MinMaxScaler |
| Education | 0.628 | Positively Skewed | MinMaxScaler |
| Family_Size | 0.080 | Normal Distribution (Symmetric) | No Transformation |
| Income | 0.023 | Normal Distribution (Symmetric) | No Transformation |
| Kidhome | 0.615 | Positively Skewed | MinMaxScaler |
| Marital_Status | 0.012 | Normal Distribution (Symmetric) | No Transformation |
| MntFishProducts | 1.907 | Highly Positively Skewed | PowerTransformer/Log |
| MntFruits | 2.062 | Highly Positively Skewed | PowerTransformer/Log |
| MntGoldProds | 1.877 | Highly Positively Skewed | PowerTransformer/Log |
| MntMeatProducts | 1.836 | Highly Positively Skewed | PowerTransformer/Log |
| MntSweetProducts | 2.082 | Highly Positively Skewed | PowerTransformer/Log |
| MntWines | 1.223 | Highly Positively Skewed | PowerTransformer/Log |
| NumCatalogPurchases | 1.445 | Highly Positively Skewed | PowerTransformer/Log |
| NumDealsPurchases | 2.415 | Highly Positively Skewed | PowerTransformer/Log |
| NumStorePurchases | 0.722 | Positively Skewed | MinMaxScaler |
| NumWebPurchases | 1.386 | Highly Positively Skewed | PowerTransformer/Log |
| NumWebVisitsMonth | 0.367 | Negatively Skewed | MinMaxScaler |
| RFM_Cat | -0.035 | Normal Distribution (Symmetric) | No Transformation |
| Recency | 0.014 | Normal Distribution (Symmetric) | No Transformation |
| Relationship_Status | -0.640 | Positively Skewed | MinMaxScaler |
| Spending_Purchase_Ratio | 1.333 | Highly Positively Skewed | PowerTransformer/Log |
| Teenhome | 0.412 | Positively Skewed | MinMaxScaler |
| Total_Children | 0.407 | Positively Skewed | MinMaxScaler |
| Total_Offers | 2.681 | Highly Positively Skewed | PowerTransformer/Log |
| Total_Purchase | 0.244 | Positively Skewed | MinMaxScaler |
| Total_Spending | 0.885 | Positively Skewed | MinMaxScaler |
| Year | -0.043 | Normal Distribution (Symmetric) | No Transformation |
| Year_Birth | -0.088 | Normal Distribution (Symmetric) | No Transformation |
| at_least_one_campaign | 2.002 | Highly Positively Skewed | PowerTransformer/Log |
| more_one_campaign | 3.636 | Highly Positively Skewed | PowerTransformer/Log |
| primary_needs | -1.251 | Positively Skewed | MinMaxScaler |
log_cols = list(result_table[result_table['Normalization Advice'] == 'PowerTransformer/Log'].index)
norm_cols = list(result_table[result_table['Normalization Advice'].isin(['MinMaxScaler','No Transformation'])].index)
print(f'log transformation: {log_cols}')
print(f'normalisasi: {norm_cols}')
log transformation: ['AcceptedCmp1', 'AcceptedCmp2', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'Complain', 'MntFishProducts', 'MntFruits', 'MntGoldProds', 'MntMeatProducts', 'MntSweetProducts', 'MntWines', 'NumCatalogPurchases', 'NumDealsPurchases', 'NumWebPurchases', 'Spending_Purchase_Ratio', 'Total_Offers', 'at_least_one_campaign', 'more_one_campaign'] normalisasi: ['Customer_Lifespan', 'Deal_Purchase_Ratio', 'Education', 'Family_Size', 'Income', 'Kidhome', 'Marital_Status', 'NumStorePurchases', 'NumWebVisitsMonth', 'RFM_Cat', 'Recency', 'Relationship_Status', 'Teenhome', 'Total_Children', 'Total_Purchase', 'Total_Spending', 'Year', 'Year_Birth', 'primary_needs']
# menggunakan powertransformer untuk transformasi features pada log_cols
from sklearn.preprocessing import PowerTransformer
# melakukan transformasi
scaler = PowerTransformer(method='yeo-johnson')
data_train[log_cols] = scaler.fit_transform(data_train[log_cols])
data_test[log_cols] = scaler.transform(data_test[log_cols])
# menampilkan distribusi fitur-fitur setelah PowerTransform
plt.figure(figsize=(12, 6))
plt.boxplot(data_train[log_cols], vert=False, labels=data_train[log_cols].columns)
plt.title('Distribusi Fitur setelah PowerTransform')
plt.xlabel('Nilai Transformasi')
plt.ylabel('Fitur')
plt.show()
# menggunakan powertransformer untuk transformasi features
from sklearn.preprocessing import MinMaxScaler
minmax = MinMaxScaler()
data_train[norm_cols] = minmax.fit_transform(data_train[norm_cols])
data_test[norm_cols] = minmax.transform(data_test[norm_cols])
# menampilkan distribusi fitur-fitur setelah Normalisasi
plt.figure(figsize=(12, 6))
plt.boxplot(data_train[norm_cols], vert=False,
labels=data_train[norm_cols].columns)
plt.title('Distribusi Fitur setelah Normalisasi')
plt.xlabel('Nilai Normalisasi')
plt.ylabel('Fitur')
plt.show()
# backup
data_before_feature_selection_train = data_train.copy()
data_before_feature_selection_test = data_test.copy()
# restore
data_train = data_before_feature_selection_train.copy()
data_train.head()
| Year_Birth | Education | Marital_Status | Income | Kidhome | Teenhome | Recency | MntWines | MntFruits | MntMeatProducts | ... | Year | Total_Purchase | Total_Spending | Total_Offers | Spending_Purchase_Ratio | Deal_Purchase_Ratio | at_least_one_campaign | more_one_campaign | primary_needs | RFM_Cat | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.178571 | 0.0 | 0.0 | 0.134665 | 0.0 | 0.0 | 0.494949 | -1.523740 | -0.004248 | -1.040166 | ... | 0.5 | 0.119048 | 0.016687 | 1.932145 | -0.991174 | 0.166667 | 2.416231 | -0.256859 | 0.0 | 0.333333 |
| 1 | 0.410714 | 0.5 | 0.0 | 0.557668 | 0.0 | 0.5 | 0.303030 | 0.756515 | 0.356628 | 0.698862 | ... | 0.5 | 0.547619 | 0.324593 | -0.512632 | 0.471277 | 0.125000 | -0.413868 | -0.256859 | 1.0 | 0.666667 |
| 2 | 0.553571 | 1.0 | 0.5 | 0.627112 | 0.0 | 0.5 | 0.595960 | 1.453882 | -1.447016 | 0.121258 | ... | 0.0 | 0.428571 | 0.428288 | 1.932145 | 1.036242 | 0.157895 | 2.416231 | -0.256859 | 1.0 | 0.666667 |
| 3 | 0.500000 | 1.0 | 0.5 | 0.245143 | 0.5 | 0.0 | 0.434343 | -0.859859 | -1.447016 | -1.365005 | ... | 0.5 | 0.095238 | 0.010330 | -0.512632 | -1.182550 | 0.200000 | -0.413868 | -0.256859 | 1.0 | 0.333333 |
| 4 | 0.517857 | 0.0 | 0.5 | 0.024088 | 0.0 | 0.5 | 0.000000 | -1.062296 | -0.380042 | -0.991477 | ... | 0.5 | 0.571429 | 0.139452 | -0.512632 | -0.453045 | 0.000000 | -0.413868 | -0.256859 | 1.0 | 0.666667 |
5 rows × 39 columns
# memisahkan feature berdasarkan tipe data cat/object dan numerik
num_cols = []
cat_cols = []
# melakukan perulangan setiap feature sesuai dengan tipe data
for col in data_before_encoding.drop(['Response','feature'], axis=1).columns:
if data_before_encoding[col].dtype == 'int64' or data_before_encoding[col].dtype == 'float64':
num_cols.append(col)
elif data_before_encoding[col].dtype == 'object' or data_before_encoding[col].dtype == 'category':
cat_cols.append(col)
# print hasilnya yang disimpan pada masing-masing feature
print(f'Total Numeric Columns : {len(num_cols)}')
print(f'Total Object Columns : {len(cat_cols)}')
Total Numeric Columns : 33 Total Object Columns : 5
# memisahkan X dan y sebagai feature dan target
X_train = data_train.drop('Response', axis=1)
y_train = data_train['Response']
X_test = data_test.drop('Response', axis=1)
y_test = data_test['Response']
# melakukan features selection menggunakan f_classif (anova)
from sklearn.feature_selection import f_classif
# menghitung nilai F-statistic dan p-value untuk setiap feature
f_statistic, p_values = f_classif(X_train[num_cols], y_train)
# menentukan tingkat signifikansi (alpha)
alpha = 0.05
# memilih feature yang memiliki p-value kurang dari 0.05 (95%, tingkat signifikansi umumnya digunakan)
selected_features_indices = np.where(p_values < alpha)[0]
selected_feature_names = X_train[num_cols].columns[selected_features_indices].tolist()
# membuat DataFrame dengan informasi F-statistic dan p-value untuk fitur yang dipilih
selected_features_anova = pd.DataFrame({
'Feature': selected_feature_names,
'F_Statistic': f_statistic[selected_features_indices],
'P_Value': p_values[selected_features_indices]
})
# mengurutkan DataFrame berdasarkan F-statistic secara descending
selected_features_anova = selected_features_anova.sort_values(by='F_Statistic', ascending=False)
# menampilkan feature yang dipilih beserta skor F-statistic dalam bentuk tabel
print("\nTabel Feature yang Dipilih:")
print(selected_features_anova)
# menampilkan skor dalam bentuk plot
plt.figure(figsize=(12, 6))
plt.bar(selected_features_anova['Feature'], -np.log10(selected_features_anova['P_Value']), color='#e9c369')
plt.axhline(-np.log10(alpha), color='red', linestyle='dashed', linewidth=2, label=f'Significance Level ({alpha})')
plt.title('Feature Importance based on ANOVA F-Statistic')
plt.xlabel('Features')
plt.ylabel('-log10(p-value)')
plt.xticks(rotation=45, ha='right')
plt.legend()
plt.show()
Tabel Feature yang Dipilih:
Feature F_Statistic P_Value
23 Total_Offers 292.373006 1.835470e-60
27 more_one_campaign 246.157183 8.332075e-52
14 AcceptedCmp5 212.448003 2.363466e-45
15 AcceptedCmp1 179.074224 7.643053e-39
22 Total_Spending 126.699997 2.281261e-28
12 AcceptedCmp3 118.700317 9.721559e-27
11 NumCatalogPurchases 102.398177 2.159944e-23
24 Spending_Purchase_Ratio 98.001782 1.750366e-22
18 Family_Size 95.648147 5.379007e-22
6 MntMeatProducts 93.846780 1.271728e-21
3 Recency 80.756569 6.823586e-19
26 at_least_one_campaign 66.710987 6.192657e-16
9 MntGoldProds 65.667004 1.030405e-15
4 MntWines 64.506086 1.816035e-15
19 Customer_Lifespan 59.696130 1.911319e-14
17 Total_Children 59.358758 2.255200e-14
13 AcceptedCmp4 54.314835 2.691016e-13
10 NumWebPurchases 53.451504 4.118075e-13
0 Income 52.134165 7.887214e-13
20 Year 46.463783 1.305505e-11
21 Total_Purchase 45.046183 2.639741e-11
2 Teenhome 44.137642 4.147375e-11
16 AcceptedCmp2 39.731354 3.733437e-10
5 MntFruits 30.385701 4.104349e-08
8 MntSweetProducts 28.313325 1.173656e-07
25 Deal_Purchase_Ratio 20.639837 5.946699e-06
7 MntFishProducts 18.802021 1.538053e-05
1 Kidhome 14.569459 1.400886e-04
X_train_anova = X_train.loc[:,selected_feature_names]
X_test_anova = X_test.loc[:,selected_feature_names]
# melakukan features selection menggunakan chi-square test
from sklearn.feature_selection import chi2
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# menghitung chi-square statistic dan p-value untuk setiap feature
chi2_stat, p_values_chi2 = chi2(X_train[cat_cols], y_train)
# menentukan tingkat signifikansi (alpha)
alpha = 0.05
# memilih feature yang memiliki p-value kurang dari 0.05 (95% confidence level)
selected_features_chi2_indices = np.where(p_values_chi2 < alpha)[0]
selected_features_chi2_names = X_train[cat_cols].columns[selected_features_chi2_indices].tolist(
)
# membuat DataFrame dengan informasi Chi-statistic dan p-value untuk fitur yang dipilih
selected_features_chi = pd.DataFrame({
'Feature': selected_features_chi2_names,
'Chi_Statistic': chi2_stat[selected_features_chi2_indices],
'P_Value': p_values_chi2[selected_features_chi2_indices]
})
# mengurutkan DataFrame berdasarkan Chi-statistic secara descending
selected_features_chi = selected_features_chi.sort_values(
by='Chi_Statistic', ascending=False)
# menampilkan feature yang dipilih beserta skor Chi-statistic dalam bentuk tabel
print("\nTabel Feature yang Dipilih:")
print(selected_features_chi)
# menampilkan skor dalam bentuk plot
plt.figure(figsize=(12, 6))
plt.bar(selected_features_chi['Feature'], -
np.log10(selected_features_chi['P_Value']), color='#e9c369')
plt.axhline(-np.log10(alpha), color='red', linestyle='dashed',
linewidth=2, label=f'Significance Level ({alpha})')
plt.title('Feature Importance based on Chi-square Statistic')
plt.xlabel('Features')
plt.ylabel('-log10(p-value)')
plt.xticks(rotation=0, ha='center')
plt.ylim(0, 10) # Mengatur skala ylim dari 0 hingga 10
plt.legend()
plt.show()
Tabel Feature yang Dipilih:
Feature Chi_Statistic P_Value
2 RFM_Cat 19.379965 0.000011
1 Relationship_Status 13.154564 0.000287
0 Education 4.374336 0.036484
X_train_chi2 = X_train.loc[:,selected_features_chi2_names]
X_test_chi2 = X_test.loc[:,selected_features_chi2_names]
# concat selected features from anova dan chi2
X_train = pd.concat([X_train_anova, X_train_chi2], axis=1)
X_test = pd.concat([X_test_anova, X_test_chi2], axis=1)
VIF mengukur seberapa jauh variabilitas suatu variabel independen dapat dijelaskan oleh variabel independen lain dalam model. Sebagai aturan umum, nilai VIF yang tinggi (biasanya di atas 10) menunjukkan adanya masalah multikolinearitas, yang dapat mempengaruhi interpretasi model dan membuat estimasi koefisien menjadi tidak stabil.
Seleksi Awal Fitur:
Engineering Fitur (Opsional):
Perhitungan VIF:
Evaluasi VIF:
Pemodelan Ulang:
Ulangi Langkah 3-5:
Evaluasi Kinerja:
# default feature selection
from statsmodels.stats.outliers_influence import variance_inflation_factor
def calculate_vif(df):
vif_data = pd.DataFrame()
vif_data["Feature"] = df.columns
vif_data["VIF"] = [variance_inflation_factor(
df.values, i) for i in range(df.shape[1])]
return vif_data
vif_results = calculate_vif(X_train)
print(f'Avg. VIF Score: {(sum(vif_results["VIF"]/len(vif_results)))}')
vif_results
Avg. VIF Score: inf
| Feature | VIF | |
|---|---|---|
| 0 | Income | 3.000721e+01 |
| 1 | Kidhome | inf |
| 2 | Teenhome | inf |
| 3 | Recency | 7.906623e+00 |
| 4 | MntWines | 1.536519e+01 |
| 5 | MntFruits | 2.799495e+00 |
| 6 | MntMeatProducts | 1.068134e+01 |
| 7 | MntFishProducts | 2.946718e+00 |
| 8 | MntSweetProducts | 2.689423e+00 |
| 9 | MntGoldProds | 2.795796e+00 |
| 10 | NumWebPurchases | 4.477282e+00 |
| 11 | NumCatalogPurchases | 6.486516e+00 |
| 12 | AcceptedCmp3 | 4.820741e+01 |
| 13 | AcceptedCmp4 | 4.646552e+01 |
| 14 | AcceptedCmp5 | 4.511825e+01 |
| 15 | AcceptedCmp1 | 4.010818e+01 |
| 16 | AcceptedCmp2 | 5.816116e+00 |
| 17 | Total_Children | inf |
| 18 | Family_Size | inf |
| 19 | Customer_Lifespan | 1.606089e+01 |
| 20 | Year | 1.268764e+01 |
| 21 | Total_Purchase | 5.257854e+01 |
| 22 | Total_Spending | 1.959583e+01 |
| 23 | Total_Offers | 2.266641e+07 |
| 24 | Spending_Purchase_Ratio | 2.694432e+01 |
| 25 | Deal_Purchase_Ratio | 1.233535e+01 |
| 26 | at_least_one_campaign | 1.684281e+07 |
| 27 | more_one_campaign | 8.183600e+06 |
| 28 | Education | 2.049095e+00 |
| 29 | Relationship_Status | inf |
| 30 | RFM_Cat | 2.594126e+01 |
drop_features = ['Total_Children', 'Kidhome', 'Teenhome', 'at_least_one_campaign', 'more_one_campaign', 'Family_Size', 'Relationship_Status',
'Income', 'Year', 'Total_Purchase', 'Spending_Purchase_Ratio', 'Total_Spending', 'Deal_Purchase_Ratio', 'Total_Offers','MntMeatProducts','MntWines']
# vif score setelah menghapus beberapa feature
vif_results = calculate_vif(X_train.drop(drop_features, axis=1))
print(f'Avg. VIF Score: {(sum(vif_results["VIF"]/len(vif_results)))}')
vif_results
Avg. VIF Score: 2.2793386425396354
| Feature | VIF | |
|---|---|---|
| 0 | Recency | 2.562749 |
| 1 | MntFruits | 2.510120 |
| 2 | MntFishProducts | 2.662851 |
| 3 | MntSweetProducts | 2.489294 |
| 4 | MntGoldProds | 2.186144 |
| 5 | NumWebPurchases | 1.853393 |
| 6 | NumCatalogPurchases | 3.031351 |
| 7 | AcceptedCmp3 | 1.103208 |
| 8 | AcceptedCmp4 | 1.280283 |
| 9 | AcceptedCmp5 | 1.372765 |
| 10 | AcceptedCmp1 | 1.306924 |
| 11 | AcceptedCmp2 | 1.119148 |
| 12 | Customer_Lifespan | 4.230985 |
| 13 | Education | 1.960772 |
| 14 | RFM_Cat | 4.520092 |
# membuat variabel untuk feature selection
selected_features = X_train.drop(drop_features, axis=1).columns
selected_features = sorted(list(selected_features))
selected_features
['AcceptedCmp1', 'AcceptedCmp2', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'Customer_Lifespan', 'Education', 'MntFishProducts', 'MntFruits', 'MntGoldProds', 'MntSweetProducts', 'NumCatalogPurchases', 'NumWebPurchases', 'RFM_Cat', 'Recency']
# backup sebelum handling imbalance
X_before_handling_imbalance_training = X_train.copy()
y_before_handling_imbalance_training = y_train.copy()
# restore point
X_train = X_before_handling_imbalance_training.copy()
y_train = y_before_handling_imbalance_training.copy()
X_train = X_train[selected_features]
X_test = X_test[selected_features]
# melakukan imbalace handling pada target
from imblearn.over_sampling import SMOTE
# menampikan jumlah kelas sebelum oversampling
print("Jumlah kelas sebelum oversampling:")
print("Kelas 0:", sum(y_train == 0))
print("Kelas 1:", sum(y_train == 1))
# melakukan oversampling dengan SMOTE
smote = SMOTE(random_state=42)
X_resampled, y_resampled = smote.fit_resample(X_train, y_train)
# menampilkan jumlah kelas setelah oversampling
print("\nJumlah kelas setelah oversampling:")
print("Kelas 0:", sum(y_resampled == 0))
print("Kelas 1:", sum(y_resampled == 1))
Jumlah kelas sebelum oversampling: Kelas 0: 1397 Kelas 1: 251 Jumlah kelas setelah oversampling: Kelas 0: 1397 Kelas 1: 1397
# mendefinisikan X (features) dan y (target), dimana X dan y hasil dari imbalance handling sebelumnya
X_train = X_resampled
y_train = y_resampled
from sklearn.model_selection import learning_curve, cross_val_score, GridSearchCV
from sklearn.metrics import precision_score, accuracy_score, make_scorer, log_loss
from sklearn.svm import SVC
from xgboost import XGBClassifier
from sklearn.ensemble import AdaBoostClassifier
X_train.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2794 entries, 0 to 2793 Data columns (total 15 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 AcceptedCmp1 2794 non-null float64 1 AcceptedCmp2 2794 non-null float64 2 AcceptedCmp3 2794 non-null float64 3 AcceptedCmp4 2794 non-null float64 4 AcceptedCmp5 2794 non-null float64 5 Customer_Lifespan 2794 non-null float64 6 Education 2794 non-null float64 7 MntFishProducts 2794 non-null float64 8 MntFruits 2794 non-null float64 9 MntGoldProds 2794 non-null float64 10 MntSweetProducts 2794 non-null float64 11 NumCatalogPurchases 2794 non-null float64 12 NumWebPurchases 2794 non-null float64 13 RFM_Cat 2794 non-null float64 14 Recency 2794 non-null float64 dtypes: float64(15) memory usage: 327.5 KB
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):
fig, axes = plt.subplots(1, 2, figsize=(14, 5))
# Learning Curve for Precision
axes[0].set_title(f"{title} - Precision")
if ylim is not None:
axes[0].set_ylim(*ylim)
axes[0].set_xlabel("Training examples")
axes[0].set_ylabel("Score")
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes, scoring=make_scorer(precision_score, average='weighted'))
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
axes[0].grid()
axes[0].fill_between(train_sizes, train_scores_mean - train_scores_std,
train_scores_mean + train_scores_std, alpha=0.1,
color="r")
axes[0].fill_between(train_sizes, test_scores_mean - test_scores_std,
test_scores_mean + test_scores_std, alpha=0.1, color="g")
axes[0].plot(train_sizes, train_scores_mean, 'o-', color="r", label="Training score")
axes[0].plot(train_sizes, test_scores_mean, 'o-', color="g", label="Cross-validation score")
axes[0].legend(loc="best")
# Learning Curve for Loss
axes[1].set_title(f"{title} - Loss")
if ylim is not None:
axes[1].set_ylim(*ylim)
axes[1].set_xlabel("Training examples")
axes[1].set_ylabel("Loss")
train_sizes, train_loss, test_loss = learning_curve(
estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes, scoring=make_scorer(log_loss, needs_proba=True))
train_loss_mean = np.mean(train_loss, axis=1)
train_loss_std = np.std(train_loss, axis=1)
test_loss_mean = np.mean(test_loss, axis=1)
test_loss_std = np.std(test_loss, axis=1)
axes[1].grid()
axes[1].fill_between(train_sizes, train_loss_mean - train_loss_std,
train_loss_mean + train_loss_std, alpha=0.1,
color="r")
axes[1].fill_between(train_sizes, test_loss_mean - test_loss_std,
test_loss_mean + test_loss_std, alpha=0.1, color="g")
axes[1].plot(train_sizes, train_loss_mean, 'o-', color="r", label="Training loss")
axes[1].plot(train_sizes, test_loss_mean, 'o-', color="g", label="Cross-validation loss")
axes[1].legend(loc="best")
plt.tight_layout()
return plt
def models_precision(X_train, y_train, X_test, y_test):
models = [
SVC(probability=True, random_state=42),
XGBClassifier(max_depth=1, random_state=42),
AdaBoostClassifier(random_state=42)
]
results_list_test = []
results_list_train = []
results_list_diff = []
results_list_diff_pct = []
for model in models:
# Cross-validation
cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='precision')
avg_cv_precision = cv_scores.mean()
model.fit(X_train, y_train)
# Learning Curve
# plot_learning_curve(model, f"{type(model).__name__}", X_train, y_train, cv=5)
# test set evaluation
y_pred_test = model.predict(X_test)
precision_test = precision_score(y_test, y_pred_test, average='weighted')
results_test = {
"Model": type(model).__name__,
"CV Precision": avg_cv_precision,
"Precision": precision_test
}
results_list_test.append(results_test)
# train set evaluation
y_pred_train = model.predict(X_train)
precision_train = precision_score(y_train, y_pred_train, average='weighted')
results_train = {
"Model": type(model).__name__,
"Precision": precision_train
}
results_list_train.append(results_train)
# diff evaluation
results_diff = {
"Model": type(model).__name__,
"Precision_Difference": precision_train - precision_test
}
results_list_diff.append(results_diff)
# diff evaluation pct
results_diff_pct = {
"Model": type(model).__name__,
"Precision_Difference": ((precision_train - precision_test) / precision_train) * 100
}
results_list_diff_pct.append(results_diff_pct)
# dataframe from test and test evaluation
results_test_df = pd.DataFrame(results_list_test)
results_test_df.set_index("Model", inplace=True)
results_train_df = pd.DataFrame(results_list_train)
results_train_df.set_index("Model", inplace=True)
# dataframe evaluations diff
results_diff_df = pd.DataFrame(results_list_diff)
results_diff_df.set_index("Model", inplace=True)
# dataframe evaluation diff pct
results_diff_pct_df = pd.DataFrame(results_list_diff_pct)
results_diff_pct_df.set_index("Model", inplace=True)
# new df
evaluation_df = pd.DataFrame(index=[type(model).__name__ for model in models])
evaluation_df['CV Precision'] = results_test_df['CV Precision']
evaluation_df['Precision_Train'] = results_train_df['Precision']
evaluation_df['Precision_Test'] = results_test_df['Precision']
evaluation_df['Diff'] = results_diff_df['Precision_Difference']
evaluation_df['Diff (%)'] = results_diff_pct_df['Precision_Difference']
return evaluation_df
def models_accuracy(X_train, y_train, X_test, y_test):
models = [
SVC(probability=True, random_state=42),
XGBClassifier(max_depth=1, random_state=42),
AdaBoostClassifier(random_state=42)
]
results_list_test = []
results_list_train = []
results_list_diff = []
results_list_diff_pct = []
for model in models:
# Cross-validation
cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
avg_cv_accuracy = cv_scores.mean()
model.fit(X_train, y_train)
# Learning Curve
# plot_learning_curve(model, f"{type(model).__name__}", X_train, y_train, cv=5)
# test set evaluation
y_pred_test = model.predict(X_test)
accuracy_test = accuracy_score(y_test, y_pred_test)
results_test = {
"Model": type(model).__name__,
"CV Accuracy": avg_cv_accuracy,
"Accuracy": accuracy_test
}
results_list_test.append(results_test)
# train set evaluation
y_pred_train = model.predict(X_train)
accuracy_train = accuracy_score(y_train, y_pred_train)
results_train = {
"Model": type(model).__name__,
"Accuracy": accuracy_train
}
results_list_train.append(results_train)
# diff evaluation
results_diff = {
"Model": type(model).__name__,
"Accuracy_Difference": accuracy_train - accuracy_test
}
results_list_diff.append(results_diff)
# diff evaluation pct
results_diff_pct = {
"Model": type(model).__name__,
"Accuracy_Difference": ((accuracy_train - accuracy_test) / accuracy_train) * 100
}
results_list_diff_pct.append(results_diff_pct)
# dataframe from test and test evaluation
results_test_df = pd.DataFrame(results_list_test)
results_test_df.set_index("Model", inplace=True)
results_train_df = pd.DataFrame(results_list_train)
results_train_df.set_index("Model", inplace=True)
# dataframe evaluations diff
results_diff_df = pd.DataFrame(results_list_diff)
results_diff_df.set_index("Model", inplace=True)
# dataframe evaluation diff pct
results_diff_pct_df = pd.DataFrame(results_list_diff_pct)
results_diff_pct_df.set_index("Model", inplace=True)
# new df
evaluation_df = pd.DataFrame(index=[type(model).__name__ for model in models])
evaluation_df['CV Accuracy'] = results_test_df['CV Accuracy']
evaluation_df['Accuracy_Train'] = results_train_df['Accuracy']
evaluation_df['Accuracy_Test'] = results_test_df['Accuracy']
evaluation_df['Diff'] = results_diff_df['Accuracy_Difference']
evaluation_df['Diff (%)'] = results_diff_pct_df['Accuracy_Difference']
return evaluation_df
def model_tuning_precision(X_train, y_train, X_test, y_test, params_list, model_type='svc'):
results_list = []
for idx, params in enumerate(params_list):
param_name = f"param_{idx+1}"
if model_type.lower() == 'svc':
model = SVC(**params, probability=True, random_state=42)
elif model_type.lower() == 'xgbclassifier':
model = XGBClassifier(**params, max_depth=1, random_state=42)
elif model_type.lower() == 'adaboost':
model = AdaBoostClassifier(**params, random_state=42)
else:
raise ValueError("Invalid model type. Supported types are 'svc', 'xgbclassifier', and 'adaboost'.")
# Cross-validation
cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='precision')
avg_cv_precision = cv_scores.mean()
model.fit(X_train, y_train)
# train set evaluation
y_pred_train = model.predict(X_train)
precision_train = precision_score(y_train, y_pred_train, average='weighted')
# test set evaluation
y_pred_test = model.predict(X_test)
precision_test = precision_score(y_test, y_pred_test, average='weighted')
results = {
"Parameter Set": param_name,
"Model": type(model).__name__,
"CV Precision": avg_cv_precision,
"Precision_Train": precision_train,
"Precision_Test": precision_test,
"Diff": precision_train - precision_test,
"Diff (%)": ((precision_train - precision_test) / precision_train) * 100,
"Parameters": str(params)
}
results_list.append(results)
# Create DataFrame from results list
evaluation_df = pd.DataFrame(results_list)
evaluation_df.set_index("Parameter Set", inplace=True)
return evaluation_df
def model_tuning_accuracy(X_train, y_train, X_test, y_test, params_list, model_type='svc'):
results_list = []
for idx, params in enumerate(params_list):
param_name = f"param_{idx+1}"
if model_type.lower() == 'svc':
model = SVC(**params, probability=True, random_state=42)
elif model_type.lower() == 'xgbclassifier':
model = XGBClassifier(**params, max_depth=1, random_state=42)
elif model_type.lower() == 'adaboost':
model = AdaBoostClassifier(**params, random_state=42)
else:
raise ValueError("Invalid model type. Supported types are 'svc', 'xgbclassifier', and 'adaboost'.")
# Cross-validation
cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
avg_cv_accuracy = cv_scores.mean()
model.fit(X_train, y_train)
# train set evaluation
y_pred_train = model.predict(X_train)
accuracy_train = accuracy_score(y_train, y_pred_train)
# test set evaluation
y_pred_test = model.predict(X_test)
accuracy_test = accuracy_score(y_test, y_pred_test)
results = {
"Parameter Set": param_name,
"Model": type(model).__name__,
"CV Accuracy": avg_cv_accuracy,
"Accuracy_Train": accuracy_train,
"Accuracy_Test": accuracy_test,
"Diff": accuracy_train - accuracy_test,
"Diff (%)": ((accuracy_train - accuracy_test) / accuracy_train) * 100,
"Parameters": str(params)
}
results_list.append(results)
# Create DataFrame from results list
evaluation_df = pd.DataFrame(results_list)
evaluation_df.set_index("Parameter Set", inplace=True)
return evaluation_df
# first metric (precision)
default_precision = models_precision(X_train, y_train, X_test, y_test)
default_precision
| CV Precision | Precision_Train | Precision_Test | Diff | Diff (%) | |
|---|---|---|---|---|---|
| SVC | 0.799378 | 0.856226 | 0.836876 | 0.019350 | 2.259904 |
| XGBClassifier | 0.847233 | 0.864785 | 0.827637 | 0.037148 | 4.295622 |
| AdaBoostClassifier | 0.872463 | 0.879967 | 0.822993 | 0.056974 | 6.474576 |
# second metric (accuracy)
default_accuracy = models_accuracy(X_train, y_train, X_test, y_test)
default_accuracy
| CV Accuracy | Accuracy_Train | Accuracy_Test | Diff | Diff (%) | |
|---|---|---|---|---|---|
| SVC | 0.832502 | 0.852899 | 0.743119 | 0.109780 | 12.871371 |
| XGBClassifier | 0.840023 | 0.864710 | 0.807339 | 0.057371 | 6.634668 |
| AdaBoostClassifier | 0.861142 | 0.879742 | 0.816514 | 0.063229 | 7.187166 |
# precision tuning
# define model and parameters
model = SVC(probability=True)
kernel = ['poly', 'rbf', 'linear']
C = [1, 2, 3, 4, 5]
gamma = ['scale', 'auto']
# define grid search
grid = dict(kernel=kernel,C=C,gamma=gamma)
grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=5, scoring='precision', error_score=0, return_train_score=True)
grid_result = grid_search.fit(X_train, y_train)
# summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means_test = grid_result.cv_results_['mean_test_score']
means_train = grid_result.cv_results_['mean_train_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean_test, mean_train, stdev, param in zip(means_test, means_train, stds, params):
print(f"{mean_test}, {mean_train}, {stdev} with: {param}")
Best: 0.874959 using {'C': 3, 'gamma': 'auto', 'kernel': 'poly'}
0.8644953563104041, 0.8929407815319182, 0.01856815437095068 with: {'C': 1, 'gamma': 'scale', 'kernel': 'poly'}
0.7993784604063702, 0.8210307612871779, 0.012063588854681057 with: {'C': 1, 'gamma': 'scale', 'kernel': 'rbf'}
0.7922502890434228, 0.793926676438712, 0.003878999368544625 with: {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}
0.8648059951888861, 0.892933514050925, 0.017988902787741075 with: {'C': 1, 'gamma': 'auto', 'kernel': 'poly'}
0.8008227825365651, 0.8226624910842218, 0.01080446522967414 with: {'C': 1, 'gamma': 'auto', 'kernel': 'rbf'}
0.7922502890434228, 0.793926676438712, 0.003878999368544625 with: {'C': 1, 'gamma': 'auto', 'kernel': 'linear'}
0.8697615356892001, 0.8999971848486954, 0.02252521870767744 with: {'C': 2, 'gamma': 'scale', 'kernel': 'poly'}
0.818454516247957, 0.844862851865334, 0.010662542618646867 with: {'C': 2, 'gamma': 'scale', 'kernel': 'rbf'}
0.7908714261187912, 0.7949764167038232, 0.00413311469160671 with: {'C': 2, 'gamma': 'scale', 'kernel': 'linear'}
0.8737489307337661, 0.9026723433894415, 0.025167587428961368 with: {'C': 2, 'gamma': 'auto', 'kernel': 'poly'}
0.8221844859344621, 0.8460160592299794, 0.013148893449438722 with: {'C': 2, 'gamma': 'auto', 'kernel': 'rbf'}
0.7908714261187912, 0.7949764167038232, 0.00413311469160671 with: {'C': 2, 'gamma': 'auto', 'kernel': 'linear'}
0.8745567055442045, 0.9058455806482624, 0.021897984490693974 with: {'C': 3, 'gamma': 'scale', 'kernel': 'poly'}
0.8294839492880092, 0.8574063148341029, 0.013612934368169327 with: {'C': 3, 'gamma': 'scale', 'kernel': 'rbf'}
0.7905774249647815, 0.7943927795369439, 0.00341690076545919 with: {'C': 3, 'gamma': 'scale', 'kernel': 'linear'}
0.8749594226083375, 0.9088298610927522, 0.02410831557897364 with: {'C': 3, 'gamma': 'auto', 'kernel': 'poly'}
0.8304118173766311, 0.8595460877422555, 0.015466872742696048 with: {'C': 3, 'gamma': 'auto', 'kernel': 'rbf'}
0.7905774249647815, 0.7943927795369439, 0.00341690076545919 with: {'C': 3, 'gamma': 'auto', 'kernel': 'linear'}
0.8719363631409017, 0.90922576634777, 0.024107700968200516 with: {'C': 4, 'gamma': 'scale', 'kernel': 'poly'}
0.8363044171567899, 0.867403054422895, 0.014970522763672054 with: {'C': 4, 'gamma': 'scale', 'kernel': 'rbf'}
0.7899151733091522, 0.795143331065292, 0.0040912185497245625 with: {'C': 4, 'gamma': 'scale', 'kernel': 'linear'}
0.8715389911950513, 0.9104298741100247, 0.02191057611185297 with: {'C': 4, 'gamma': 'auto', 'kernel': 'poly'}
0.8384168740408899, 0.8698470491527861, 0.01785773479953582 with: {'C': 4, 'gamma': 'auto', 'kernel': 'rbf'}
0.7899151733091522, 0.795143331065292, 0.0040912185497245625 with: {'C': 4, 'gamma': 'auto', 'kernel': 'linear'}
0.8707849019506877, 0.9109999360143208, 0.020548024900351028 with: {'C': 5, 'gamma': 'scale', 'kernel': 'poly'}
0.8402555911334215, 0.8724592736138066, 0.016895992603648068 with: {'C': 5, 'gamma': 'scale', 'kernel': 'rbf'}
0.7910988656371538, 0.79503972135662, 0.004456659072693344 with: {'C': 5, 'gamma': 'scale', 'kernel': 'linear'}
0.8718026094639788, 0.9132210382199716, 0.02051176454686488 with: {'C': 5, 'gamma': 'auto', 'kernel': 'poly'}
0.843024860734786, 0.8747285893185079, 0.01649835504382095 with: {'C': 5, 'gamma': 'auto', 'kernel': 'rbf'}
0.7910988656371538, 0.79503972135662, 0.004456659072693344 with: {'C': 5, 'gamma': 'auto', 'kernel': 'linear'}
# evaluation results - precision
svc_tuning_precision = model_tuning_precision(X_train, y_train, X_test, y_test, params, model_type='svc')
svc_tuning_precision
| Model | CV Precision | Precision_Train | Precision_Test | Diff | Diff (%) | Parameters | |
|---|---|---|---|---|---|---|---|
| Parameter Set | |||||||
| param_1 | SVC | 0.864495 | 0.814202 | 0.804183 | 0.010019 | 1.230524 | {'C': 1, 'gamma': 'scale', 'kernel': 'poly'} |
| param_2 | SVC | 0.799378 | 0.856226 | 0.836876 | 0.019350 | 2.259904 | {'C': 1, 'gamma': 'scale', 'kernel': 'rbf'} |
| param_3 | SVC | 0.792250 | 0.807795 | 0.846378 | -0.038583 | -4.776376 | {'C': 1, 'gamma': 'scale', 'kernel': 'linear'} |
| param_4 | SVC | 0.864806 | 0.817847 | 0.804183 | 0.013664 | 1.670685 | {'C': 1, 'gamma': 'auto', 'kernel': 'poly'} |
| param_5 | SVC | 0.800823 | 0.857977 | 0.833500 | 0.024477 | 2.852910 | {'C': 1, 'gamma': 'auto', 'kernel': 'rbf'} |
| param_6 | SVC | 0.792250 | 0.807795 | 0.846378 | -0.038583 | -4.776376 | {'C': 1, 'gamma': 'auto', 'kernel': 'linear'} |
| param_7 | SVC | 0.869762 | 0.832848 | 0.811858 | 0.020990 | 2.520239 | {'C': 2, 'gamma': 'scale', 'kernel': 'poly'} |
| param_8 | SVC | 0.818455 | 0.876420 | 0.821935 | 0.054485 | 6.216769 | {'C': 2, 'gamma': 'scale', 'kernel': 'rbf'} |
| param_9 | SVC | 0.790871 | 0.807753 | 0.847020 | -0.039267 | -4.861221 | {'C': 2, 'gamma': 'scale', 'kernel': 'linear'} |
| param_10 | SVC | 0.873749 | 0.837537 | 0.814110 | 0.023427 | 2.797151 | {'C': 2, 'gamma': 'auto', 'kernel': 'poly'} |
| param_11 | SVC | 0.822184 | 0.878579 | 0.825903 | 0.052676 | 5.995626 | {'C': 2, 'gamma': 'auto', 'kernel': 'rbf'} |
| param_12 | SVC | 0.790871 | 0.807753 | 0.847020 | -0.039267 | -4.861221 | {'C': 2, 'gamma': 'auto', 'kernel': 'linear'} |
| param_13 | SVC | 0.874557 | 0.840083 | 0.810913 | 0.029170 | 3.472247 | {'C': 3, 'gamma': 'scale', 'kernel': 'poly'} |
| param_14 | SVC | 0.829484 | 0.888494 | 0.825154 | 0.063340 | 7.128920 | {'C': 3, 'gamma': 'scale', 'kernel': 'rbf'} |
| param_15 | SVC | 0.790577 | 0.808051 | 0.846378 | -0.038327 | -4.743093 | {'C': 3, 'gamma': 'scale', 'kernel': 'linear'} |
| param_16 | SVC | 0.874959 | 0.844516 | 0.807559 | 0.036957 | 4.376101 | {'C': 3, 'gamma': 'auto', 'kernel': 'poly'} |
| param_17 | SVC | 0.830412 | 0.888974 | 0.820530 | 0.068444 | 7.699262 | {'C': 3, 'gamma': 'auto', 'kernel': 'rbf'} |
| param_18 | SVC | 0.790577 | 0.808051 | 0.846378 | -0.038327 | -4.743093 | {'C': 3, 'gamma': 'auto', 'kernel': 'linear'} |
| param_19 | SVC | 0.871936 | 0.847879 | 0.810913 | 0.036966 | 4.359848 | {'C': 4, 'gamma': 'scale', 'kernel': 'poly'} |
| param_20 | SVC | 0.836304 | 0.896172 | 0.825212 | 0.070960 | 7.918164 | {'C': 4, 'gamma': 'scale', 'kernel': 'rbf'} |
| param_21 | SVC | 0.789915 | 0.807673 | 0.846378 | -0.038705 | -4.792119 | {'C': 4, 'gamma': 'scale', 'kernel': 'linear'} |
| param_22 | SVC | 0.871539 | 0.852063 | 0.806533 | 0.045530 | 5.343496 | {'C': 4, 'gamma': 'auto', 'kernel': 'poly'} |
| param_23 | SVC | 0.838417 | 0.895851 | 0.821919 | 0.073932 | 8.252699 | {'C': 4, 'gamma': 'auto', 'kernel': 'rbf'} |
| param_24 | SVC | 0.789915 | 0.807673 | 0.846378 | -0.038705 | -4.792119 | {'C': 4, 'gamma': 'auto', 'kernel': 'linear'} |
| param_25 | SVC | 0.870785 | 0.853597 | 0.809886 | 0.043711 | 5.120801 | {'C': 5, 'gamma': 'scale', 'kernel': 'poly'} |
| param_26 | SVC | 0.840256 | 0.898973 | 0.822615 | 0.076358 | 8.493901 | {'C': 5, 'gamma': 'scale', 'kernel': 'rbf'} |
| param_27 | SVC | 0.791099 | 0.808051 | 0.846378 | -0.038327 | -4.743093 | {'C': 5, 'gamma': 'scale', 'kernel': 'linear'} |
| param_28 | SVC | 0.871803 | 0.857475 | 0.816533 | 0.040941 | 4.774655 | {'C': 5, 'gamma': 'auto', 'kernel': 'poly'} |
| param_29 | SVC | 0.843025 | 0.901236 | 0.822615 | 0.078621 | 8.723685 | {'C': 5, 'gamma': 'auto', 'kernel': 'rbf'} |
| param_30 | SVC | 0.791099 | 0.808051 | 0.846378 | -0.038327 | -4.743093 | {'C': 5, 'gamma': 'auto', 'kernel': 'linear'} |
# filter parameter - precision
filter = (svc_tuning_precision['CV Precision'] > 0.8) & (svc_tuning_precision['Diff'] > 0)
svc_tuning_precision[filter].sort_values(by=['CV Precision','Diff'], ascending=[False,True]).nlargest(3, columns=['CV Precision'])
| Model | CV Precision | Precision_Train | Precision_Test | Diff | Diff (%) | Parameters | |
|---|---|---|---|---|---|---|---|
| Parameter Set | |||||||
| param_16 | SVC | 0.874959 | 0.844516 | 0.807559 | 0.036957 | 4.376101 | {'C': 3, 'gamma': 'auto', 'kernel': 'poly'} |
| param_13 | SVC | 0.874557 | 0.840083 | 0.810913 | 0.029170 | 3.472247 | {'C': 3, 'gamma': 'scale', 'kernel': 'poly'} |
| param_10 | SVC | 0.873749 | 0.837537 | 0.814110 | 0.023427 | 2.797151 | {'C': 2, 'gamma': 'auto', 'kernel': 'poly'} |
# get parameter
params_svc_precision = svc_tuning_precision.loc['param_10']
params_svc_precision['Parameters']
"{'C': 2, 'gamma': 'auto', 'kernel': 'poly'}"
# Define XGBClassifier model and parameters
model = XGBClassifier(max_depth=1)
objective = ['reg:squarederror','binary:logistic','reg:logistic']
learning_rate = [0.3, 0.5, 0.8, 1.0]
n_estimators = [50, 100, 150, 200]
subsample = [0.8, 0.9, 1.0]
colsample_bytree = [0.8, 0.9, 1.0]
gamma = [1,2,3]
# Define grid search
grid = dict(objective=objective, learning_rate=learning_rate, n_estimators=n_estimators, subsample=subsample, colsample_bytree=colsample_bytree, gamma=gamma)
grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=5, scoring='precision', error_score=0, return_train_score=True)
grid_result = grid_search.fit(X_train, y_train)
# Summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means_test = grid_result.cv_results_['mean_test_score']
means_train = grid_result.cv_results_['mean_train_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean_test, mean_train, stdev, param in zip(means_test, means_train, stds, params):
print(f"{mean_test}, {mean_train}, {stdev} with: {param}")
Best: 0.910658 using {'colsample_bytree': 1.0, 'gamma': 2, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8299457744641299, 0.8389208943404004, 0.014888192993427939 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8293090267767853, 0.8398972118929592, 0.008221253825128958 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8293335554208031, 0.8373391538639574, 0.010104068645862965 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8228856680385596, 0.8342904887499492, 0.01706061828633937 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8216838405322011, 0.8355145170747902, 0.016266046642534795 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8189310282017626, 0.8326457004936584, 0.01001571499292632 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8228856680385596, 0.8342904887499492, 0.01706061828633937 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8216838405322011, 0.8355145170747902, 0.016266046642534795 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8189310282017626, 0.8326457004936584, 0.01001571499292632 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8548548879714687, 0.8700321016290957, 0.009469492081913793 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8534811698500115, 0.8674051385767629, 0.014341680290427889 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8466588430991904, 0.8612556131114321, 0.012060413520002597 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8471510688632365, 0.8677982753797234, 0.012090710654316427 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.8}
0.850585282221234, 0.8662002276131936, 0.01307394721397286 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8487908939834, 0.8656186629733265, 0.0150989689456641 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8471510688632365, 0.8677982753797234, 0.012090710654316427 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.8}
0.850585282221234, 0.8662002276131936, 0.01307394721397286 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8487908939834, 0.8656186629733265, 0.0150989689456641 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8596845384698699, 0.8778725872576445, 0.012406523522338451 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8648407858816329, 0.8741077790023116, 0.017637774236310313 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8466588430991904, 0.8612556131114321, 0.012060413520002597 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8661294095456397, 0.8897400255519325, 0.011462317919958533 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.8}
0.863519233775935, 0.8883583257668981, 0.010654815888523876 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8642082748775876, 0.886151598000745, 0.01270817284923519 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8661294095456397, 0.8897400255519325, 0.011462317919958533 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.8}
0.863519233775935, 0.8883583257668981, 0.010654815888523876 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8642082748775876, 0.886151598000745, 0.01270817284923519 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8591720436679118, 0.8754161605611447, 0.014354792164482617 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8593776333250485, 0.8779163674446764, 0.015304647200532345 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8466588430991904, 0.8612556131114321, 0.012060413520002597 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8762556598440427, 0.8993492725355271, 0.014485364396322344 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8741487752764157, 0.8969663784863509, 0.008327512976452695 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8775306507444981, 0.8969020397272317, 0.012578463327935566 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8762556598440427, 0.8993492725355271, 0.014485364396322344 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8741487752764157, 0.8969663784863509, 0.008327512976452695 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.9}
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0.8905346758196593, 0.9215339504988457, 0.013838531755500831 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.9}
0.874877430901595, 0.9044065081956905, 0.014722430735983807 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 1.0}
0.9051841264872778, 0.9265349383092293, 0.013345511879383223 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8905346758196593, 0.9215339504988457, 0.013838531755500831 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.9}
0.874877430901595, 0.9044065081956905, 0.014722430735983807 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8271171348083242, 0.8493471195411813, 0.007070903395014026 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8319741380660467, 0.8519953448383228, 0.016339204151552388 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8211156286868011, 0.8402909247527685, 0.021244409097678878 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.901321593552305, 0.9256612211809445, 0.015169276961400677 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8925073144281681, 0.9212773973131603, 0.013573054364919413 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.9}
0.874877430901595, 0.9044065081956905, 0.014722430735983807 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 1.0}
0.901321593552305, 0.9256612211809445, 0.015169276961400677 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8925073144281681, 0.9212773973131603, 0.013573054364919413 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.9}
0.874877430901595, 0.9044065081956905, 0.014722430735983807 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8485935359639768, 0.8609769462491654, 0.026480437078480447 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8368696557137548, 0.8496959169208035, 0.023913133610723274 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8340819434564676, 0.8523541036437466, 0.0184633131148186 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8674866954277691, 0.8909117791851566, 0.023479268189447875 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8704899764230987, 0.8934099383586146, 0.02015526920293098 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8713752637036626, 0.8971574760371489, 0.018151263863591484 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8674866954277691, 0.8909117791851566, 0.023479268189447875 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8704899764230987, 0.8934099383586146, 0.02015526920293098 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8713752637036626, 0.8971574760371489, 0.018151263863591484 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8513440184776678, 0.8648843167262925, 0.022485952156250013 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8396716620260343, 0.8510561025614963, 0.02501487961734432 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8340819434564676, 0.8523541036437466, 0.0184633131148186 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8919297721443067, 0.9202742628147863, 0.019338913037478495 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8955218953129384, 0.9218254898140321, 0.014722852489633849 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8788701478318526, 0.9077477388651344, 0.02247169490147497 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8919297721443067, 0.9202742628147863, 0.019338913037478495 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8955218953129384, 0.9218254898140321, 0.014722852489633849 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8788701478318526, 0.9077477388651344, 0.02247169490147497 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8460593334096789, 0.8625128965624752, 0.013377831002613633 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8430671632788181, 0.8576221300175668, 0.023228210809970787 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8340819434564676, 0.8523541036437466, 0.0184633131148186 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.9101360197593298, 0.9346848048357088, 0.015228605259143572 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8978595310468606, 0.9287365369386084, 0.017626192378520717 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8788701478318526, 0.9077477388651344, 0.02247169490147497 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 1.0}
0.9101360197593298, 0.9346848048357088, 0.015228605259143572 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8978595310468606, 0.9287365369386084, 0.017626192378520717 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8788701478318526, 0.9077477388651344, 0.02247169490147497 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8409334140223935, 0.8575324028723953, 0.017228771524378086 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8411308810218898, 0.849178146569405, 0.024250055654701567 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8340819434564676, 0.8523541036437466, 0.0184633131148186 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.9076538543403861, 0.9390165047775716, 0.017881679646635718 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8974378783432557, 0.930424879822716, 0.01594900940566984 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8788701478318526, 0.9077477388651344, 0.02247169490147497 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 1.0}
0.9076538543403861, 0.9390165047775716, 0.017881679646635718 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8974378783432557, 0.930424879822716, 0.01594900940566984 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8788701478318526, 0.9077477388651344, 0.02247169490147497 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 1.0}
xgb_tuning_precision = model_tuning_precision(X_train, y_train, X_test, y_test, params, model_type='xgbclassifier')
xgb_tuning_precision
| Model | CV Precision | Precision_Train | Precision_Test | Diff | Diff (%) | Parameters | |
|---|---|---|---|---|---|---|---|
| Parameter Set | |||||||
| param_1 | XGBClassifier | 0.825169 | 0.850661 | 0.835631 | 0.015030 | 1.766848 | {'colsample_bytree': 0.8, 'gamma': 1, 'learnin... |
| param_2 | XGBClassifier | 0.828413 | 0.845522 | 0.833142 | 0.012380 | 1.464172 | {'colsample_bytree': 0.8, 'gamma': 1, 'learnin... |
| param_3 | XGBClassifier | 0.826268 | 0.840944 | 0.827472 | 0.013472 | 1.601998 | {'colsample_bytree': 0.8, 'gamma': 1, 'learnin... |
| param_4 | XGBClassifier | 0.820744 | 0.837713 | 0.827491 | 0.010222 | 1.220218 | {'colsample_bytree': 0.8, 'gamma': 1, 'learnin... |
| param_5 | XGBClassifier | 0.816850 | 0.836493 | 0.829966 | 0.006527 | 0.780327 | {'colsample_bytree': 0.8, 'gamma': 1, 'learnin... |
| ... | ... | ... | ... | ... | ... | ... | ... |
| param_1292 | XGBClassifier | 0.900328 | 0.915380 | 0.840051 | 0.075329 | 8.229262 | {'colsample_bytree': 1.0, 'gamma': 3, 'learnin... |
| param_1293 | XGBClassifier | 0.878870 | 0.895441 | 0.823811 | 0.071630 | 7.999425 | {'colsample_bytree': 1.0, 'gamma': 3, 'learnin... |
| param_1294 | XGBClassifier | 0.905373 | 0.922569 | 0.819691 | 0.102878 | 11.151284 | {'colsample_bytree': 1.0, 'gamma': 3, 'learnin... |
| param_1295 | XGBClassifier | 0.900328 | 0.915380 | 0.840051 | 0.075329 | 8.229262 | {'colsample_bytree': 1.0, 'gamma': 3, 'learnin... |
| param_1296 | XGBClassifier | 0.878870 | 0.895441 | 0.823811 | 0.071630 | 7.999425 | {'colsample_bytree': 1.0, 'gamma': 3, 'learnin... |
1296 rows × 7 columns
# filter parameter - precision
filter = (xgb_tuning_precision['CV Precision'] > 0.8) & (xgb_tuning_precision['Diff'] > 0)
xgb_tuning_precision[filter].sort_values(by=['CV Precision','Diff'], ascending=[False,True]).nlargest(3, columns=['CV Precision'])
| Model | CV Precision | Precision_Train | Precision_Test | Diff | Diff (%) | Parameters | |
|---|---|---|---|---|---|---|---|
| Parameter Set | |||||||
| param_140 | XGBClassifier | 0.912467 | 0.922357 | 0.818055 | 0.104302 | 11.308190 | {'colsample_bytree': 0.8, 'gamma': 1, 'learnin... |
| param_143 | XGBClassifier | 0.912467 | 0.922357 | 0.818055 | 0.104302 | 11.308190 | {'colsample_bytree': 0.8, 'gamma': 1, 'learnin... |
| param_1147 | XGBClassifier | 0.912229 | 0.924031 | 0.835093 | 0.088938 | 9.625034 | {'colsample_bytree': 1.0, 'gamma': 2, 'learnin... |
# get parameter
params_xgb_precision = xgb_tuning_precision.loc['param_140']
params_xgb_precision['Parameters']
"{'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.9}"
# Initialize XGBoost model
params = {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.9}
xgb_model = XGBClassifier(**params, max_depth=1, random_state=42)
# Perform cross-validation with precision as the scoring metric
precision_scores = cross_val_score(xgb_model, X_train, y_train, cv=5, scoring='precision')
# Print mean and standard deviation of precision scores
print("Mean Precision:", np.mean(precision_scores))
print("Standard Deviation of Precision:", np.std(precision_scores))
# Fit the model on the entire training data
xgb_model.fit(X_train, y_train)
# Feature importances
importance_xgb = pd.Series(xgb_model.feature_importances_, index=X_train.columns).sort_values(ascending=True)
# Plotting (using barh for horizontal bar plot)
ax = importance_xgb.plot.barh(align="center", color='#e9c369')
ax.set_title("XGB Classifier Feature Importances")
ax.figure.tight_layout()
plt.show()
/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. and should_run_async(code)
Mean Precision: 0.9124665259093071 Standard Deviation of Precision: 0.016099837171287733
# Define AdaBoostClassifier model and parameters
model = AdaBoostClassifier(random_state=42)
learning_rate = [0.01, 0.1, 0.2, 0.3, 0.5, 0.8, 1.0]
n_estimators = [50, 100, 150, 200]
algorithm = ['SAMME', 'SAMME.R']
# Define grid search
grid = dict(learning_rate=learning_rate, n_estimators=n_estimators, algorithm=algorithm)
grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=5, scoring='precision', error_score=0, return_train_score=True)
grid_result = grid_search.fit(X_train, y_train)
# Summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means_test = grid_result.cv_results_['mean_test_score']
means_train = grid_result.cv_results_['mean_train_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean_test, mean_train, stdev, param in zip(means_test, means_train, stds, params):
print(f"{mean_test}, {mean_train}, {stdev} with: {param}")
Best: 0.908271 using {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 200}
0.6492129025796463, 0.6483929230586379, 0.016845244587131936 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 50}
0.6481665781698287, 0.6536249086884554, 0.015391772893054802 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 100}
0.6978192522770351, 0.7011184776316066, 0.023461272691963353 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 150}
0.7143417916689243, 0.7218193697536923, 0.02187046474265906 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 200}
0.7741031546092639, 0.7910114003195097, 0.020136252906739694 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 50}
0.7920923098178263, 0.808250360899665, 0.022840059522987156 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 100}
0.7897202908849246, 0.8101847186221949, 0.019488280391017073 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 150}
0.7981894861638571, 0.8122734741370502, 0.017207304914300448 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 200}
0.7922655606464397, 0.8067944593762009, 0.026223375631359194 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 50}
0.7926709666042697, 0.8075427745566068, 0.01956882844555728 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 100}
0.8053032028562879, 0.8193491631051957, 0.02114187021855238 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 150}
0.8088078920584441, 0.8261938141175433, 0.022245577041637588 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 200}
0.8027496143255508, 0.8162847569153773, 0.014800250522407958 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 50}
0.8115298990560648, 0.8218829459586697, 0.02169416571727745 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 100}
0.8235857476444908, 0.8342885774868224, 0.015784528624701036 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 150}
0.8332554594576814, 0.8443920380169654, 0.02022584692235086 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 200}
0.8109491608928519, 0.8256750451810884, 0.016063004696718102 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 50}
0.8383133802442699, 0.8459384336022439, 0.020463931825548558 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 100}
0.8520541924726521, 0.868237492388252, 0.01909883243025282 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 150}
0.8554486452478848, 0.8741264281055463, 0.016249676363641396 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 200}
0.8385508869975627, 0.8510792469833289, 0.01507930171908834 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 50}
0.8608792877132567, 0.8861656732332917, 0.019810730521170997 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 100}
0.8663340131964483, 0.8880402808701776, 0.015449005182397132 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 150}
0.8780231974140461, 0.8941777917310516, 0.01620907660722986 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 200}
0.8492118876912322, 0.8684037807169277, 0.017496843397156774 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 50}
0.8632976683046565, 0.8880285672993706, 0.01868966734811718 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 100}
0.8821573122055067, 0.9032788502681613, 0.017446611247299147 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 150}
0.8827708932514792, 0.9072559009234059, 0.015755686953888043 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 200}
0.6490939587056719, 0.6534428171807594, 0.014436633770513825 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 50}
0.7099828256270964, 0.7174151620720879, 0.015419428150022596 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 100}
0.7229098710982346, 0.7319493649714768, 0.016061284278344252 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 150}
0.7423688566704706, 0.752377811059427, 0.019765496022307358 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 200}
0.7852388342132902, 0.7950171406858104, 0.016860003031678637 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 50}
0.8017634695936557, 0.8132264625833647, 0.009684776139796762 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 100}
0.8183604486250415, 0.8312745791243936, 0.010758529494339204 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 150}
0.8296336991140434, 0.8440187844352256, 0.013945072807370496 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 200}
0.7995899610490653, 0.813977564473379, 0.007451251330903823 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 50}
0.833774227844047, 0.8464073695128302, 0.016464257032295194 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 100}
0.8492455343764036, 0.8632262232467707, 0.014447931352461764 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 150}
0.8582696093958649, 0.8786438595274436, 0.016366243283525 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 200}
0.8224710072356645, 0.835541500387432, 0.010516732917087757 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 50}
0.84998718027335, 0.8684274308355654, 0.014484054528523504 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 100}
0.8664717361345332, 0.8882350871717314, 0.01875774503066364 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 150}
0.8764391092845185, 0.8979913560745288, 0.01639436499593488 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 200}
0.8428472052663828, 0.863361847485766, 0.017747013628598252 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 50}
0.8706493507552402, 0.89498604155281, 0.012884893159994764 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 100}
0.8862269073301036, 0.9081727144945514, 0.014684080395021903 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 150}
0.8955467113070703, 0.915046486262384, 0.013327170691222683 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 200}
0.8605723896741674, 0.885645353691421, 0.017953642566500748 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 50}
0.8911474857423715, 0.9105714212160256, 0.01755219172638399 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 100}
0.8989724265285923, 0.9219293146720087, 0.01560168887937801 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 150}
0.9001824427320889, 0.9288254631291938, 0.013488662528999029 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 200}
0.8724634280697028, 0.8948320894885159, 0.015675246369269036 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 50}
0.8929582286758455, 0.9172776025228355, 0.018418534128540092 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 100}
0.9032525488063451, 0.9242118947069929, 0.019295069138344296 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 150}
0.9082709380712878, 0.9358567904100219, 0.02648982805265192 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 200}
adb_tuning_precision = model_tuning_precision(X_train, y_train, X_test, y_test, params, model_type='adaboost')
adb_tuning_precision
| Model | CV Precision | Precision_Train | Precision_Test | Diff | Diff (%) | Parameters | |
|---|---|---|---|---|---|---|---|
| Parameter Set | |||||||
| param_1 | AdaBoostClassifier | 0.649213 | 0.711296 | 0.802507 | -0.091211 | -12.823232 | {'algorithm': 'SAMME', 'learning_rate': 0.01, ... |
| param_2 | AdaBoostClassifier | 0.648167 | 0.711701 | 0.790427 | -0.078726 | -11.061692 | {'algorithm': 'SAMME', 'learning_rate': 0.01, ... |
| param_3 | AdaBoostClassifier | 0.697819 | 0.736949 | 0.797057 | -0.060109 | -8.156405 | {'algorithm': 'SAMME', 'learning_rate': 0.01, ... |
| param_4 | AdaBoostClassifier | 0.714342 | 0.737002 | 0.793539 | -0.056537 | -7.671175 | {'algorithm': 'SAMME', 'learning_rate': 0.01, ... |
| param_5 | AdaBoostClassifier | 0.774103 | 0.781595 | 0.807536 | -0.025941 | -3.318927 | {'algorithm': 'SAMME', 'learning_rate': 0.1, '... |
| param_6 | AdaBoostClassifier | 0.792092 | 0.802792 | 0.810650 | -0.007859 | -0.978912 | {'algorithm': 'SAMME', 'learning_rate': 0.1, '... |
| param_7 | AdaBoostClassifier | 0.789720 | 0.820168 | 0.817211 | 0.002957 | 0.360545 | {'algorithm': 'SAMME', 'learning_rate': 0.1, '... |
| param_8 | AdaBoostClassifier | 0.798189 | 0.825283 | 0.823851 | 0.001432 | 0.173518 | {'algorithm': 'SAMME', 'learning_rate': 0.1, '... |
| param_9 | AdaBoostClassifier | 0.792266 | 0.810844 | 0.812586 | -0.001742 | -0.214828 | {'algorithm': 'SAMME', 'learning_rate': 0.2, '... |
| param_10 | AdaBoostClassifier | 0.792671 | 0.816348 | 0.824742 | -0.008394 | -1.028224 | {'algorithm': 'SAMME', 'learning_rate': 0.2, '... |
| param_11 | AdaBoostClassifier | 0.805303 | 0.835488 | 0.832016 | 0.003472 | 0.415603 | {'algorithm': 'SAMME', 'learning_rate': 0.2, '... |
| param_12 | AdaBoostClassifier | 0.808808 | 0.842031 | 0.828284 | 0.013747 | 1.632651 | {'algorithm': 'SAMME', 'learning_rate': 0.2, '... |
| param_13 | AdaBoostClassifier | 0.802750 | 0.817125 | 0.816566 | 0.000559 | 0.068402 | {'algorithm': 'SAMME', 'learning_rate': 0.3, '... |
| param_14 | AdaBoostClassifier | 0.811530 | 0.837601 | 0.824233 | 0.013368 | 1.595944 | {'algorithm': 'SAMME', 'learning_rate': 0.3, '... |
| param_15 | AdaBoostClassifier | 0.823586 | 0.847405 | 0.824233 | 0.023172 | 2.734462 | {'algorithm': 'SAMME', 'learning_rate': 0.3, '... |
| param_16 | AdaBoostClassifier | 0.833255 | 0.855825 | 0.823515 | 0.032309 | 3.775241 | {'algorithm': 'SAMME', 'learning_rate': 0.3, '... |
| param_17 | AdaBoostClassifier | 0.810949 | 0.833202 | 0.826708 | 0.006493 | 0.779341 | {'algorithm': 'SAMME', 'learning_rate': 0.5, '... |
| param_18 | AdaBoostClassifier | 0.838313 | 0.857308 | 0.819657 | 0.037652 | 4.391845 | {'algorithm': 'SAMME', 'learning_rate': 0.5, '... |
| param_19 | AdaBoostClassifier | 0.852054 | 0.871906 | 0.815523 | 0.056383 | 6.466587 | {'algorithm': 'SAMME', 'learning_rate': 0.5, '... |
| param_20 | AdaBoostClassifier | 0.855449 | 0.876164 | 0.821865 | 0.054299 | 6.197341 | {'algorithm': 'SAMME', 'learning_rate': 0.5, '... |
| param_21 | AdaBoostClassifier | 0.838551 | 0.855509 | 0.815478 | 0.040032 | 4.679263 | {'algorithm': 'SAMME', 'learning_rate': 0.8, '... |
| param_22 | AdaBoostClassifier | 0.860879 | 0.880849 | 0.810752 | 0.070097 | 7.957845 | {'algorithm': 'SAMME', 'learning_rate': 0.8, '... |
| param_23 | AdaBoostClassifier | 0.866334 | 0.887425 | 0.824434 | 0.062991 | 7.098191 | {'algorithm': 'SAMME', 'learning_rate': 0.8, '... |
| param_24 | AdaBoostClassifier | 0.878023 | 0.891516 | 0.832926 | 0.058590 | 6.571986 | {'algorithm': 'SAMME', 'learning_rate': 0.8, '... |
| param_25 | AdaBoostClassifier | 0.849212 | 0.871961 | 0.819175 | 0.052785 | 6.053645 | {'algorithm': 'SAMME', 'learning_rate': 1.0, '... |
| param_26 | AdaBoostClassifier | 0.863298 | 0.882214 | 0.808512 | 0.073702 | 8.354178 | {'algorithm': 'SAMME', 'learning_rate': 1.0, '... |
| param_27 | AdaBoostClassifier | 0.882157 | 0.887904 | 0.816892 | 0.071012 | 7.997676 | {'algorithm': 'SAMME', 'learning_rate': 1.0, '... |
| param_28 | AdaBoostClassifier | 0.882771 | 0.893509 | 0.828283 | 0.065227 | 7.300039 | {'algorithm': 'SAMME', 'learning_rate': 1.0, '... |
| param_29 | AdaBoostClassifier | 0.649094 | 0.711996 | 0.790427 | -0.078431 | -11.015625 | {'algorithm': 'SAMME.R', 'learning_rate': 0.01... |
| param_30 | AdaBoostClassifier | 0.709983 | 0.737859 | 0.790033 | -0.052173 | -7.070882 | {'algorithm': 'SAMME.R', 'learning_rate': 0.01... |
| param_31 | AdaBoostClassifier | 0.722910 | 0.752078 | 0.796505 | -0.044426 | -5.907148 | {'algorithm': 'SAMME.R', 'learning_rate': 0.01... |
| param_32 | AdaBoostClassifier | 0.742369 | 0.769917 | 0.798014 | -0.028098 | -3.649448 | {'algorithm': 'SAMME.R', 'learning_rate': 0.01... |
| param_33 | AdaBoostClassifier | 0.785239 | 0.810770 | 0.813240 | -0.002470 | -0.304647 | {'algorithm': 'SAMME.R', 'learning_rate': 0.1,... |
| param_34 | AdaBoostClassifier | 0.801763 | 0.818932 | 0.816745 | 0.002187 | 0.267035 | {'algorithm': 'SAMME.R', 'learning_rate': 0.1,... |
| param_35 | AdaBoostClassifier | 0.818360 | 0.836725 | 0.820971 | 0.015754 | 1.882816 | {'algorithm': 'SAMME.R', 'learning_rate': 0.1,... |
| param_36 | AdaBoostClassifier | 0.829634 | 0.850601 | 0.823515 | 0.027086 | 3.184370 | {'algorithm': 'SAMME.R', 'learning_rate': 0.1,... |
| param_37 | AdaBoostClassifier | 0.799590 | 0.823803 | 0.826708 | -0.002906 | -0.352699 | {'algorithm': 'SAMME.R', 'learning_rate': 0.2,... |
| param_38 | AdaBoostClassifier | 0.833774 | 0.851398 | 0.822653 | 0.028745 | 3.376218 | {'algorithm': 'SAMME.R', 'learning_rate': 0.2,... |
| param_39 | AdaBoostClassifier | 0.849246 | 0.866599 | 0.824764 | 0.041835 | 4.827492 | {'algorithm': 'SAMME.R', 'learning_rate': 0.2,... |
| param_40 | AdaBoostClassifier | 0.858270 | 0.876163 | 0.819831 | 0.056332 | 6.429434 | {'algorithm': 'SAMME.R', 'learning_rate': 0.2,... |
| param_41 | AdaBoostClassifier | 0.822471 | 0.844017 | 0.828308 | 0.015709 | 1.861217 | {'algorithm': 'SAMME.R', 'learning_rate': 0.3,... |
| param_42 | AdaBoostClassifier | 0.849987 | 0.862321 | 0.824764 | 0.037557 | 4.355310 | {'algorithm': 'SAMME.R', 'learning_rate': 0.3,... |
| param_43 | AdaBoostClassifier | 0.866472 | 0.878339 | 0.820858 | 0.057480 | 6.544208 | {'algorithm': 'SAMME.R', 'learning_rate': 0.3,... |
| param_44 | AdaBoostClassifier | 0.876439 | 0.887056 | 0.826518 | 0.060538 | 6.824629 | {'algorithm': 'SAMME.R', 'learning_rate': 0.3,... |
| param_45 | AdaBoostClassifier | 0.842847 | 0.862922 | 0.821503 | 0.041420 | 4.799913 | {'algorithm': 'SAMME.R', 'learning_rate': 0.5,... |
| param_46 | AdaBoostClassifier | 0.870649 | 0.885182 | 0.817561 | 0.067621 | 7.639203 | {'algorithm': 'SAMME.R', 'learning_rate': 0.5,... |
| param_47 | AdaBoostClassifier | 0.886227 | 0.893469 | 0.823208 | 0.070261 | 7.863885 | {'algorithm': 'SAMME.R', 'learning_rate': 0.5,... |
| param_48 | AdaBoostClassifier | 0.895547 | 0.898486 | 0.825688 | 0.072798 | 8.102283 | {'algorithm': 'SAMME.R', 'learning_rate': 0.5,... |
| param_49 | AdaBoostClassifier | 0.860572 | 0.881286 | 0.822969 | 0.058318 | 6.617339 | {'algorithm': 'SAMME.R', 'learning_rate': 0.8,... |
| param_50 | AdaBoostClassifier | 0.891147 | 0.897105 | 0.809602 | 0.087503 | 9.753906 | {'algorithm': 'SAMME.R', 'learning_rate': 0.8,... |
| param_51 | AdaBoostClassifier | 0.898972 | 0.906498 | 0.818208 | 0.088290 | 9.739665 | {'algorithm': 'SAMME.R', 'learning_rate': 0.8,... |
| param_52 | AdaBoostClassifier | 0.900182 | 0.912634 | 0.822355 | 0.090279 | 9.892101 | {'algorithm': 'SAMME.R', 'learning_rate': 0.8,... |
| param_53 | AdaBoostClassifier | 0.872463 | 0.879967 | 0.822993 | 0.056974 | 6.474576 | {'algorithm': 'SAMME.R', 'learning_rate': 1.0,... |
| param_54 | AdaBoostClassifier | 0.892958 | 0.901083 | 0.816345 | 0.084738 | 9.403998 | {'algorithm': 'SAMME.R', 'learning_rate': 1.0,... |
| param_55 | AdaBoostClassifier | 0.903253 | 0.910859 | 0.831003 | 0.079856 | 8.767125 | {'algorithm': 'SAMME.R', 'learning_rate': 1.0,... |
| param_56 | AdaBoostClassifier | 0.908271 | 0.918675 | 0.843316 | 0.075359 | 8.203003 | {'algorithm': 'SAMME.R', 'learning_rate': 1.0,... |
# filter parameter - precision
filter = (adb_tuning_precision['CV Precision'] > 0.8) & (adb_tuning_precision['Diff'] > 0)
adb_tuning_precision[filter].sort_values(by=['CV Precision','Diff'], ascending=[False,True]).nlargest(3, columns=['CV Precision'])
| Model | CV Precision | Precision_Train | Precision_Test | Diff | Diff (%) | Parameters | |
|---|---|---|---|---|---|---|---|
| Parameter Set | |||||||
| param_56 | AdaBoostClassifier | 0.908271 | 0.918675 | 0.843316 | 0.075359 | 8.203003 | {'algorithm': 'SAMME.R', 'learning_rate': 1.0,... |
| param_55 | AdaBoostClassifier | 0.903253 | 0.910859 | 0.831003 | 0.079856 | 8.767125 | {'algorithm': 'SAMME.R', 'learning_rate': 1.0,... |
| param_52 | AdaBoostClassifier | 0.900182 | 0.912634 | 0.822355 | 0.090279 | 9.892101 | {'algorithm': 'SAMME.R', 'learning_rate': 0.8,... |
# get parameter
params_adb_precision = adb_tuning_precision.loc['param_56']
params_adb_precision['Parameters']
"{'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 200}"
# Initialize Adaboost with DecisionTreeClassifier as base estimator
params = {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 200}
adaboost_model = AdaBoostClassifier(**params, random_state=42)
# Perform cross-validation with precision as the scoring metric
precision_scores = cross_val_score(adaboost_model, X_train, y_train, cv=5, scoring='precision')
# Print mean and standard deviation of precision scores
print("Mean Precision:", np.mean(precision_scores))
print("Standard Deviation of Precision:", np.std(precision_scores))
# Fit the model on the entire training data
adaboost_model.fit(X_train, y_train)
# Feature importances
importance_adb = pd.Series(adaboost_model.feature_importances_, index=X_train.columns).sort_values(ascending=True)
# Plotting (using barh for horizontal bar plot)
ax = importance_adb.plot.barh(align="center", color='#e9c369')
ax.set_title("Adaboost Feature Importances")
ax.figure.tight_layout()
plt.show()
Mean Precision: 0.9082709380712878 Standard Deviation of Precision: 0.02648982805265192
# accuracy tuning
# define model and parameters
model = SVC(probability=True)
kernel = ['poly', 'rbf', 'linear']
C = [1, 2, 3, 4, 5]
gamma = ['scale', 'auto']
# define grid search
grid = dict(kernel=kernel,C=C,gamma=gamma)
grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=5, scoring='accuracy', error_score=0, return_train_score=True)
grid_result = grid_search.fit(X_train, y_train)
# summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means_test = grid_result.cv_results_['mean_test_score']
means_train = grid_result.cv_results_['mean_train_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean_test, mean_train, stdev, param in zip(means_test, means_train, stds, params):
print(f"{mean_test}, {mean_train}, {stdev} with: {param}")
Best: 0.872946 using {'C': 5, 'gamma': 'auto', 'kernel': 'rbf'}
0.771293464391739, 0.788385539854246, 0.01600741715998074 with: {'C': 1, 'gamma': 'scale', 'kernel': 'poly'}
0.8325017151723829, 0.851914892765525, 0.01823377390974064 with: {'C': 1, 'gamma': 'scale', 'kernel': 'rbf'}
0.8060117593500941, 0.8078022435397182, 0.007238425945776351 with: {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}
0.777734177134027, 0.794380785439003, 0.013134682520941026 with: {'C': 1, 'gamma': 'auto', 'kernel': 'poly'}
0.8342912651239732, 0.8537044818767935, 0.016771402944234844 with: {'C': 1, 'gamma': 'auto', 'kernel': 'rbf'}
0.8060117593500941, 0.8078022435397182, 0.007238425945776351 with: {'C': 1, 'gamma': 'auto', 'kernel': 'linear'}
0.794916036701483, 0.8137073233202466, 0.010315091279536347 with: {'C': 2, 'gamma': 'scale', 'kernel': 'poly'}
0.8507562788132932, 0.8716895783057794, 0.016993674201332572 with: {'C': 2, 'gamma': 'scale', 'kernel': 'rbf'}
0.8045787087797592, 0.8086970180851873, 0.006991492873876753 with: {'C': 2, 'gamma': 'scale', 'kernel': 'linear'}
0.8009996088765782, 0.817197056104501, 0.01250702581961604 with: {'C': 2, 'gamma': 'auto', 'kernel': 'poly'}
0.8532607510852073, 0.8733001164591613, 0.018802791600030466 with: {'C': 2, 'gamma': 'auto', 'kernel': 'rbf'}
0.8045787087797592, 0.8086970180851873, 0.006991492873876753 with: {'C': 2, 'gamma': 'auto', 'kernel': 'linear'}
0.8042196446547534, 0.8246237888847535, 0.012067969072769765 with: {'C': 3, 'gamma': 'scale', 'kernel': 'poly'}
0.8600611691384386, 0.8838585601485555, 0.019320435290910504 with: {'C': 3, 'gamma': 'scale', 'kernel': 'rbf'}
0.8049364905328897, 0.8082496308124527, 0.008066089430052357 with: {'C': 3, 'gamma': 'scale', 'kernel': 'linear'}
0.8070825398657357, 0.8281133215673562, 0.012325743005626664 with: {'C': 3, 'gamma': 'auto', 'kernel': 'poly'}
0.8614929373368984, 0.885648149259824, 0.01984938016979657 with: {'C': 3, 'gamma': 'auto', 'kernel': 'rbf'}
0.8049364905328897, 0.8082496308124527, 0.008066089430052357 with: {'C': 3, 'gamma': 'auto', 'kernel': 'linear'}
0.8077987445579342, 0.8306188343678589, 0.01238102989712471 with: {'C': 4, 'gamma': 'scale', 'kernel': 'poly'}
0.8665031642526015, 0.8911958074701948, 0.01919770484314012 with: {'C': 4, 'gamma': 'scale', 'kernel': 'rbf'}
0.8042209270266284, 0.8088759489820829, 0.007702322075514746 with: {'C': 4, 'gamma': 'scale', 'kernel': 'linear'}
0.8113784856470527, 0.8360770071196167, 0.013880934002412498 with: {'C': 4, 'gamma': 'auto', 'kernel': 'poly'}
0.867577150697931, 0.8930749220604067, 0.021445271075986268 with: {'C': 4, 'gamma': 'auto', 'kernel': 'rbf'}
0.8042209270266284, 0.8088759489820829, 0.007702322075514746 with: {'C': 4, 'gamma': 'auto', 'kernel': 'linear'}
0.8113772032751777, 0.8396558251591808, 0.014442193893815264 with: {'C': 5, 'gamma': 'scale', 'kernel': 'poly'}
0.8693673418354588, 0.8960274219303406, 0.02111147735680577 with: {'C': 5, 'gamma': 'scale', 'kernel': 'rbf'}
0.8052942722860201, 0.8088759489820829, 0.009070098513506958 with: {'C': 5, 'gamma': 'scale', 'kernel': 'linear'}
0.8171029936971422, 0.845024672533647, 0.01799443680801489 with: {'C': 5, 'gamma': 'auto', 'kernel': 'poly'}
0.8729458005527022, 0.8976380801447135, 0.02269325086550227 with: {'C': 5, 'gamma': 'auto', 'kernel': 'rbf'}
0.8052942722860201, 0.8088759489820829, 0.009070098513506958 with: {'C': 5, 'gamma': 'auto', 'kernel': 'linear'}
# evaluation results - accuracy
svc_tuning_accuracy = model_tuning_accuracy(X_train, y_train, X_test, y_test, params, model_type='svc')
svc_tuning_accuracy
| Model | CV Accuracy | Accuracy_Train | Accuracy_Test | Diff | Diff (%) | Parameters | |
|---|---|---|---|---|---|---|---|
| Parameter Set | |||||||
| param_1 | SVC | 0.771293 | 0.796349 | 0.795872 | 0.000478 | 0.059994 | {'C': 1, 'gamma': 'scale', 'kernel': 'poly'} |
| param_2 | SVC | 0.832502 | 0.852899 | 0.743119 | 0.109780 | 12.871371 | {'C': 1, 'gamma': 'scale', 'kernel': 'rbf'} |
| param_3 | SVC | 0.806012 | 0.807087 | 0.768349 | 0.038738 | 4.799731 | {'C': 1, 'gamma': 'scale', 'kernel': 'linear'} |
| param_4 | SVC | 0.777734 | 0.800644 | 0.795872 | 0.004773 | 0.596105 | {'C': 1, 'gamma': 'auto', 'kernel': 'poly'} |
| param_5 | SVC | 0.834291 | 0.854331 | 0.740826 | 0.113505 | 13.285841 | {'C': 1, 'gamma': 'auto', 'kernel': 'rbf'} |
| param_6 | SVC | 0.806012 | 0.807087 | 0.768349 | 0.038738 | 4.799731 | {'C': 1, 'gamma': 'auto', 'kernel': 'linear'} |
| param_7 | SVC | 0.794916 | 0.818898 | 0.807339 | 0.011558 | 1.411433 | {'C': 2, 'gamma': 'scale', 'kernel': 'poly'} |
| param_8 | SVC | 0.850756 | 0.874374 | 0.738532 | 0.135842 | 15.535869 | {'C': 2, 'gamma': 'scale', 'kernel': 'rbf'} |
| param_9 | SVC | 0.804579 | 0.807087 | 0.770642 | 0.036444 | 4.515552 | {'C': 2, 'gamma': 'scale', 'kernel': 'linear'} |
| param_10 | SVC | 0.801000 | 0.824266 | 0.807339 | 0.016927 | 2.053564 | {'C': 2, 'gamma': 'auto', 'kernel': 'poly'} |
| param_11 | SVC | 0.853261 | 0.876521 | 0.743119 | 0.133402 | 15.219468 | {'C': 2, 'gamma': 'auto', 'kernel': 'rbf'} |
| param_12 | SVC | 0.804579 | 0.807087 | 0.770642 | 0.036444 | 4.515552 | {'C': 2, 'gamma': 'auto', 'kernel': 'linear'} |
| param_13 | SVC | 0.804220 | 0.827845 | 0.800459 | 0.027387 | 3.308186 | {'C': 3, 'gamma': 'scale', 'kernel': 'poly'} |
| param_14 | SVC | 0.860061 | 0.886543 | 0.750000 | 0.136543 | 15.401696 | {'C': 3, 'gamma': 'scale', 'kernel': 'rbf'} |
| param_15 | SVC | 0.804936 | 0.807445 | 0.768349 | 0.039096 | 4.841930 | {'C': 3, 'gamma': 'scale', 'kernel': 'linear'} |
| param_16 | SVC | 0.807083 | 0.832856 | 0.798165 | 0.034691 | 4.165303 | {'C': 3, 'gamma': 'auto', 'kernel': 'poly'} |
| param_17 | SVC | 0.861493 | 0.886901 | 0.743119 | 0.143781 | 16.211653 | {'C': 3, 'gamma': 'auto', 'kernel': 'rbf'} |
| param_18 | SVC | 0.804936 | 0.807445 | 0.768349 | 0.039096 | 4.841930 | {'C': 3, 'gamma': 'auto', 'kernel': 'linear'} |
| param_19 | SVC | 0.807799 | 0.837867 | 0.800459 | 0.037408 | 4.464688 | {'C': 4, 'gamma': 'scale', 'kernel': 'poly'} |
| param_20 | SVC | 0.866503 | 0.894417 | 0.759174 | 0.135242 | 15.120727 | {'C': 4, 'gamma': 'scale', 'kernel': 'rbf'} |
| param_21 | SVC | 0.804221 | 0.807087 | 0.768349 | 0.038738 | 4.799731 | {'C': 4, 'gamma': 'scale', 'kernel': 'linear'} |
| param_22 | SVC | 0.811378 | 0.842520 | 0.795872 | 0.046648 | 5.536740 | {'C': 4, 'gamma': 'auto', 'kernel': 'poly'} |
| param_23 | SVC | 0.867577 | 0.894059 | 0.756881 | 0.137178 | 15.343284 | {'C': 4, 'gamma': 'auto', 'kernel': 'rbf'} |
| param_24 | SVC | 0.804221 | 0.807087 | 0.768349 | 0.038738 | 4.799731 | {'C': 4, 'gamma': 'auto', 'kernel': 'linear'} |
| param_25 | SVC | 0.811377 | 0.844667 | 0.798165 | 0.046502 | 5.505365 | {'C': 5, 'gamma': 'scale', 'kernel': 'poly'} |
| param_26 | SVC | 0.869367 | 0.897280 | 0.759174 | 0.138106 | 15.391582 | {'C': 5, 'gamma': 'scale', 'kernel': 'rbf'} |
| param_27 | SVC | 0.805294 | 0.807445 | 0.768349 | 0.039096 | 4.841930 | {'C': 5, 'gamma': 'scale', 'kernel': 'linear'} |
| param_28 | SVC | 0.817103 | 0.849320 | 0.802752 | 0.046568 | 5.482937 | {'C': 5, 'gamma': 'auto', 'kernel': 'poly'} |
| param_29 | SVC | 0.872946 | 0.899785 | 0.759174 | 0.140611 | 15.627167 | {'C': 5, 'gamma': 'auto', 'kernel': 'rbf'} |
| param_30 | SVC | 0.805294 | 0.807445 | 0.768349 | 0.039096 | 4.841930 | {'C': 5, 'gamma': 'auto', 'kernel': 'linear'} |
# filter parameter - accuracy
filter = (svc_tuning_accuracy['CV Accuracy'] > 0.8) & (svc_tuning_accuracy['Diff (%)'] < 15)
svc_tuning_accuracy[filter].sort_values(by=['CV Accuracy','Diff'], ascending=[False,True]).nlargest(3, columns=['CV Accuracy'])
| Model | CV Accuracy | Accuracy_Train | Accuracy_Test | Diff | Diff (%) | Parameters | |
|---|---|---|---|---|---|---|---|
| Parameter Set | |||||||
| param_5 | SVC | 0.834291 | 0.854331 | 0.740826 | 0.113505 | 13.285841 | {'C': 1, 'gamma': 'auto', 'kernel': 'rbf'} |
| param_2 | SVC | 0.832502 | 0.852899 | 0.743119 | 0.109780 | 12.871371 | {'C': 1, 'gamma': 'scale', 'kernel': 'rbf'} |
| param_28 | SVC | 0.817103 | 0.849320 | 0.802752 | 0.046568 | 5.482937 | {'C': 5, 'gamma': 'auto', 'kernel': 'poly'} |
# get parameter
params_svc_accuracy = svc_tuning_precision.loc['param_5']
params_svc_accuracy['Parameters']
/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. and should_run_async(code)
"{'C': 1, 'gamma': 'auto', 'kernel': 'rbf'}"
# Define XGBClassifier model and parameters
model = XGBClassifier(max_depth=1)
objective = ['reg:squarederror','binary:logistic','reg:logistic']
learning_rate = [0.3, 0.5, 0.8, 1.0]
n_estimators = [50, 100, 150, 200]
subsample = [0.8, 0.9, 1.0]
colsample_bytree = [0.8, 0.9, 1.0]
gamma = [1,2,3]
# Define grid search
grid = dict(objective=objective, learning_rate=learning_rate, n_estimators=n_estimators, subsample=subsample, colsample_bytree=colsample_bytree, gamma=gamma)
grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=5, scoring='accuracy', error_score=0, return_train_score=True)
grid_result = grid_search.fit(X_train, y_train)
# Summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means_test = grid_result.cv_results_['mean_test_score']
means_train = grid_result.cv_results_['mean_train_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean_test, mean_train, stdev, param in zip(means_test, means_train, stds, params):
print(f"{mean_test}, {mean_train}, {stdev} with: {param}")
Best: 0.882618 using {'colsample_bytree': 1.0, 'gamma': 2, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8342906239380359, 0.8498574475833724, 0.03424037737398742 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8324985092426953, 0.8499473332452887, 0.03417726205834697 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8339309186270928, 0.8477994821369256, 0.03606534289675377 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8239130295394361, 0.8422516238249032, 0.034306099403451785 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8246266694878848, 0.843594425968392, 0.03019681210137752 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8217612095331525, 0.8404625149575985, 0.03089184164594709 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8239130295394361, 0.8422516238249032, 0.034306099403451785 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8246266694878848, 0.843594425968392, 0.03019681210137752 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8217612095331525, 0.8404625149575985, 0.03089184164594709 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8496835747398389, 0.8723166568616858, 0.049585989510799205 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8464628977757259, 0.8696322531846178, 0.04754692889481234 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8410929655490795, 0.8651581403352904, 0.04760347967101518 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8418110937990907, 0.869363556686797, 0.04605576101869412 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8461038336507205, 0.8690058149539966, 0.042632958081117084 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8400196202896877, 0.8675742477178406, 0.04756464620092013 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8418110937990907, 0.869363556686797, 0.04605576101869412 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8461038336507205, 0.8690058149539966, 0.042632958081117084 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8400196202896877, 0.8675742477178406, 0.04756464620092013 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8511153429382986, 0.8776853441548307, 0.057969333512023935 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8521886881976904, 0.8725849931765337, 0.04714671048234928 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8410929655490795, 0.8651581403352904, 0.04760347967101518 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8532665217586448, 0.8860062511755972, 0.057703937939536994 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8518360359320599, 0.883858760250207, 0.058837625381218395 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8493277165445207, 0.8821589767601943, 0.05313790769173715 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8532665217586448, 0.8860062511755972, 0.057703937939536994 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8518360359320599, 0.883858760250207, 0.058837625381218395 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8493277165445207, 0.8821589767601943, 0.05313790769173715 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8504017029898499, 0.8777751497760862, 0.05696491425749697 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8486108706663845, 0.8766117187531265, 0.053907646498420926 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8410929655490795, 0.8651581403352904, 0.04760347967101518 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8611415674431429, 0.8943279586029703, 0.05970551101342759 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8607812209462622, 0.891911611098438, 0.0620925505234507 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8597059521290579, 0.8899433312122559, 0.06081593727192171 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8611415674431429, 0.8943279586029703, 0.05970551101342759 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8607812209462622, 0.891911611098438, 0.0620925505234507 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8597059521290579, 0.8899433312122559, 0.06081593727192171 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8389456338443585, 0.8678431443173131, 0.052189959383707074 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8439609902475619, 0.86793186938965, 0.04839248346855253 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8464616154038509, 0.8676634930544717, 0.047711474465645325 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8418104526131532, 0.8665897075714464, 0.043660429294280996 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8407377485396991, 0.8648896839594513, 0.046694997048139134 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8421688755522215, 0.8665006223161367, 0.047799188254988696 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8418104526131532, 0.8665897075714464, 0.043660429294280996 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8407377485396991, 0.8648896839594513, 0.046694997048139134 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8421688755522215, 0.8665006223161367, 0.047799188254988696 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8482556536570038, 0.8781323312242619, 0.056179137236159554 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.847898513089811, 0.8783115022431396, 0.05736824431323805 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8450317707632037, 0.8690051746287114, 0.0511035058479586 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8575611851680869, 0.8909272710536952, 0.06340822093832747 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.8}
0.854339225832099, 0.8881545425075938, 0.06071729193863114 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8557735587743089, 0.8871696021578963, 0.06110235706864902 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8575611851680869, 0.8909272710536952, 0.06340822093832747 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.8}
0.854339225832099, 0.8881545425075938, 0.06071729193863114 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8557735587743089, 0.8871696021578963, 0.06110235706864902 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8471823083976122, 0.8824274731563634, 0.061437225828470235 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8525509582523837, 0.8780433260096128, 0.055631975513188166 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8450317707632037, 0.8690051746287114, 0.0511035058479586 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8686581901885727, 0.9045277801122971, 0.07043720343021796 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8665076525541643, 0.900680745818876, 0.06587659809324685 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8640018979103751, 0.8962963185298131, 0.06762575743102071 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8686581901885727, 0.9045277801122971, 0.07043720343021796 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8665076525541643, 0.900680745818876, 0.06587659809324685 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8640018979103751, 0.8962963185298131, 0.06762575743102071 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8536249446977129, 0.8843065877465752, 0.059444948700159525 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8521918941273778, 0.8797435097029291, 0.057143518984593 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8450317707632037, 0.8690051746287114, 0.0511035058479586 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8761754541199404, 0.9122231293497096, 0.07411941700145414 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8715223677714301, 0.9126709168257475, 0.07262721027811118 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8690153307557658, 0.9048862822313735, 0.07616424259020113 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 1.0}
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0.8325074858458205, 0.8506628967515496, 0.040312668941808114 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8196209308737441, 0.8419834876117067, 0.039672303396653605 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8786824911356044, 0.9305664477554598, 0.08891150538207061 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.8}
0.87152493251518, 0.9210823898540459, 0.08801286365255921 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8582825193477858, 0.8984440495771853, 0.0734723425698143 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8786824911356044, 0.9305664477554598, 0.08891150538207061 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.8}
0.87152493251518, 0.9210823898540459, 0.08801286365255921 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8582825193477858, 0.8984440495771853, 0.0734723425698143 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 1.0}
xgb_tuning_accuracy = model_tuning_accuracy(X_train, y_train, X_test, y_test, params, model_type='xgbclassifier')
xgb_tuning_accuracy
| Model | CV Accuracy | Accuracy_Train | Accuracy_Test | Diff | Diff (%) | Parameters | |
|---|---|---|---|---|---|---|---|
| Parameter Set | |||||||
| param_1 | XGBClassifier | 0.833222 | 0.850036 | 0.798165 | 0.051871 | 6.102173 | {'colsample_bytree': 0.8, 'gamma': 1, 'learnin... |
| param_2 | XGBClassifier | 0.832500 | 0.845025 | 0.791284 | 0.053741 | 6.359652 | {'colsample_bytree': 0.8, 'gamma': 1, 'learnin... |
| param_3 | XGBClassifier | 0.831070 | 0.840730 | 0.788991 | 0.051739 | 6.154093 | {'colsample_bytree': 0.8, 'gamma': 1, 'learnin... |
| param_4 | XGBClassifier | 0.821051 | 0.837151 | 0.782110 | 0.055041 | 6.574793 | {'colsample_bytree': 0.8, 'gamma': 1, 'learnin... |
| param_5 | XGBClassifier | 0.818185 | 0.835719 | 0.782110 | 0.053609 | 6.414750 | {'colsample_bytree': 0.8, 'gamma': 1, 'learnin... |
| ... | ... | ... | ... | ... | ... | ... | ... |
| param_1292 | XGBClassifier | 0.869739 | 0.915175 | 0.848624 | 0.066552 | 7.271997 | {'colsample_bytree': 1.0, 'gamma': 3, 'learnin... |
| param_1293 | XGBClassifier | 0.858283 | 0.895132 | 0.832569 | 0.062564 | 6.989314 | {'colsample_bytree': 1.0, 'gamma': 3, 'learnin... |
| param_1294 | XGBClassifier | 0.877971 | 0.922334 | 0.834862 | 0.087471 | 9.483682 | {'colsample_bytree': 1.0, 'gamma': 3, 'learnin... |
| param_1295 | XGBClassifier | 0.869739 | 0.915175 | 0.848624 | 0.066552 | 7.271997 | {'colsample_bytree': 1.0, 'gamma': 3, 'learnin... |
| param_1296 | XGBClassifier | 0.858283 | 0.895132 | 0.832569 | 0.062564 | 6.989314 | {'colsample_bytree': 1.0, 'gamma': 3, 'learnin... |
1296 rows × 7 columns
# filter parameter xgb - accuracy
filter = (xgb_tuning_accuracy['CV Accuracy'] > 0.8) & (xgb_tuning_accuracy['Diff'] > 0)
xgb_tuning_accuracy[filter].sort_values(by=['CV Accuracy','Diff'], ascending=[False,True]).nlargest(3, columns=['CV Accuracy'])
| Model | CV Accuracy | Accuracy_Train | Accuracy_Test | Diff | Diff (%) | Parameters | |
|---|---|---|---|---|---|---|---|
| Parameter Set | |||||||
| param_1003 | XGBClassifier | 0.879048 | 0.924123 | 0.850917 | 0.073206 | 7.921638 | {'colsample_bytree': 1.0, 'gamma': 1, 'learnin... |
| param_1006 | XGBClassifier | 0.879048 | 0.924123 | 0.850917 | 0.073206 | 7.921638 | {'colsample_bytree': 1.0, 'gamma': 1, 'learnin... |
| param_247 | XGBClassifier | 0.878325 | 0.918754 | 0.837156 | 0.081599 | 8.881427 | {'colsample_bytree': 0.8, 'gamma': 2, 'learnin... |
# get parameter
params_xgb_accuracy = xgb_tuning_accuracy.loc['param_1006']
params_xgb_accuracy['Parameters']
"{'colsample_bytree': 1.0, 'gamma': 1, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.8}"
# Define AdaBoostClassifier model and parameters
model = AdaBoostClassifier(random_state=42)
learning_rate = [0.01, 0.1, 0.2, 0.3, 0.5, 0.8, 1.0]
n_estimators = [50, 100, 150, 200]
algorithm = ['SAMME', 'SAMME.R']
# Define grid search
grid = dict(learning_rate=learning_rate, n_estimators=n_estimators, algorithm=algorithm)
grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=5, scoring='accuracy', error_score=0, return_train_score=True)
grid_result = grid_search.fit(X_train, y_train)
# Summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means_test = grid_result.cv_results_['mean_test_score']
means_train = grid_result.cv_results_['mean_train_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean_test, mean_train, stdev, param in zip(means_test, means_train, stds, params):
print(f"{mean_test}, {mean_train}, {stdev} with: {param}")
Best: 0.872244 using {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 150}
0.6929142542045768, 0.6924660927751296, 0.022290396790059352 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 50}
0.6893357954873334, 0.6955979237452625, 0.019427278743142755 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 100}
0.723326985592552, 0.7269175541174916, 0.022983702303155166 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 150}
0.7329986342739531, 0.7397999383686913, 0.022843292094760735 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 200}
0.776671732035573, 0.7899962781092794, 0.029176806883379917 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 50}
0.7967145632561987, 0.8095028274363376, 0.024384156915168185 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 100}
0.7995768172812434, 0.8185402984716236, 0.02507499853432255 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 150}
0.8078083623469972, 0.8220300712762082, 0.02554976194217574 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 200}
0.7949230897467956, 0.8079818547822294, 0.02532379002983386 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 50}
0.8053019665172704, 0.8220298311542263, 0.028239867597260212 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 100}
0.8174684696815231, 0.8334828092671076, 0.033223842246403554 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 150}
0.8221234795878456, 0.8403720289907273, 0.033551284150194746 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 200}
0.8031571995562994, 0.8162134364257042, 0.023864466986957194 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 50}
0.8253428741800833, 0.8354506889499864, 0.03173676303585906 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 100}
0.8339328421849052, 0.847709556454679, 0.036074851526161335 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 150}
0.8414481825584602, 0.8564784910734653, 0.04005016191334462 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 200}
0.8206897878315733, 0.8360776874652324, 0.0340868434394063 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 50}
0.8464628977757259, 0.8580890292268473, 0.038882707178386246 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 100}
0.85183347118831, 0.8716000528268362, 0.05144847569407908 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 150}
0.8518347535601849, 0.8768794547630195, 0.05330350611944286 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 200}
0.8418130173569033, 0.8589843640569409, 0.040426129931662405 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 50}
0.8511185488679862, 0.8800118860381074, 0.05738634406452961 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 100}
0.8564884810946326, 0.8835910242403141, 0.060681169161536874 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 150}
0.8643609620353807, 0.8864539185906442, 0.0613767439205307 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 200}
0.8493328460320209, 0.8724051418120405, 0.053225232874192376 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 50}
0.8568494687774507, 0.8840378512284239, 0.06780680730972728 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 100}
0.8657991420932156, 0.891732640181212, 0.07015451355231311 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 150}
0.8683010496213797, 0.8974595494511212, 0.07379998273016547 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 200}
0.6904097819326627, 0.6954188327670456, 0.01824768136217668 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 50}
0.7304935208161014, 0.7372049401095756, 0.020262502511329127 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 100}
0.7469546873897961, 0.7546536040308476, 0.025795815496947108 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 150}
0.7623553324228494, 0.7715641145702017, 0.027664189232246186 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 200}
0.7985002660921641, 0.8095031475989802, 0.024724269128119168 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 50}
0.8103102698751611, 0.8264144985652712, 0.027465015150397558 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 100}
0.8228364783503567, 0.8413573695437282, 0.03334279687343186 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 150}
0.8314264463551787, 0.8537050421614178, 0.040555611605451555 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 200}
0.8088785016767013, 0.8275781697102127, 0.02345346571473472 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 50}
0.8339328421849052, 0.8556738823322247, 0.04124050430340257 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 100}
0.8428837978725451, 0.8690060550759785, 0.05033212262601407 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 150}
0.8486147177820097, 0.8779539606119909, 0.062058201680318704 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 200}
0.8249850924269528, 0.8443095492510195, 0.03361266003644814 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 50}
0.8457479754554024, 0.8730328206729018, 0.052751905746885104 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 100}
0.8529113047492641, 0.884754375222613, 0.0616095333330887 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 150}
0.8600682221837511, 0.891554269568941, 0.06547038997225749 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 200}
0.8378703650271543, 0.8690956605955826, 0.04667312317080158 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 50}
0.857206609344644, 0.8888699459325335, 0.06825361527914772 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 100}
0.8672283455479255, 0.898533134832495, 0.07061499684419591 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 150}
0.8683004084354422, 0.9042599640617434, 0.07919705569257635 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 200}
0.8457531049429023, 0.8822487823814498, 0.06578808134188835 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 50}
0.8700893172010952, 0.903812976992312, 0.07208159711744441 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 100}
0.8704503048839133, 0.913924313551284, 0.08920646019723158 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 150}
0.8708080866370439, 0.9208139734985373, 0.09482543335119589 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 200}
0.8611422086290803, 0.8884226787207903, 0.06767385021534016 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 50}
0.8700931643167202, 0.9085555862378089, 0.07929995167700161 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 100}
0.8722437019511288, 0.917771587966687, 0.09380053624971159 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 150}
0.8718891261276858, 0.92546697722443, 0.10553848227519459 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 200}
adb_tuning_accuracy = model_tuning_accuracy(X_train, y_train, X_test, y_test, params, model_type='adaboost')
adb_tuning_accuracy
| Model | CV Accuracy | Accuracy_Train | Accuracy_Test | Diff | Diff (%) | Parameters | |
|---|---|---|---|---|---|---|---|
| Parameter Set | |||||||
| param_1 | AdaBoostClassifier | 0.692914 | 0.692198 | 0.527523 | 0.164675 | 23.790120 | {'algorithm': 'SAMME', 'learning_rate': 0.01, ... |
| param_2 | AdaBoostClassifier | 0.689336 | 0.694345 | 0.525229 | 0.169116 | 24.356143 | {'algorithm': 'SAMME', 'learning_rate': 0.01, ... |
| param_3 | AdaBoostClassifier | 0.723327 | 0.734431 | 0.651376 | 0.083055 | 11.308725 | {'algorithm': 'SAMME', 'learning_rate': 0.01, ... |
| param_4 | AdaBoostClassifier | 0.732999 | 0.734789 | 0.649083 | 0.085706 | 11.664067 | {'algorithm': 'SAMME', 'learning_rate': 0.01, ... |
| param_5 | AdaBoostClassifier | 0.776672 | 0.780601 | 0.715596 | 0.065005 | 8.327549 | {'algorithm': 'SAMME', 'learning_rate': 0.1, '... |
| param_6 | AdaBoostClassifier | 0.796715 | 0.802792 | 0.745413 | 0.057379 | 7.147415 | {'algorithm': 'SAMME', 'learning_rate': 0.1, '... |
| param_7 | AdaBoostClassifier | 0.799577 | 0.818898 | 0.740826 | 0.078072 | 9.533786 | {'algorithm': 'SAMME', 'learning_rate': 0.1, '... |
| param_8 | AdaBoostClassifier | 0.807808 | 0.823193 | 0.745413 | 0.077780 | 9.448544 | {'algorithm': 'SAMME', 'learning_rate': 0.1, '... |
| param_9 | AdaBoostClassifier | 0.794923 | 0.810308 | 0.743119 | 0.067189 | 8.291730 | {'algorithm': 'SAMME', 'learning_rate': 0.2, '... |
| param_10 | AdaBoostClassifier | 0.805302 | 0.816034 | 0.766055 | 0.049979 | 6.124658 | {'algorithm': 'SAMME', 'learning_rate': 0.2, '... |
| param_11 | AdaBoostClassifier | 0.817468 | 0.834646 | 0.772936 | 0.061710 | 7.393543 | {'algorithm': 'SAMME', 'learning_rate': 0.2, '... |
| param_12 | AdaBoostClassifier | 0.822123 | 0.841804 | 0.784404 | 0.057400 | 6.818714 | {'algorithm': 'SAMME', 'learning_rate': 0.2, '... |
| param_13 | AdaBoostClassifier | 0.803157 | 0.816750 | 0.747706 | 0.069044 | 8.453473 | {'algorithm': 'SAMME', 'learning_rate': 0.3, '... |
| param_14 | AdaBoostClassifier | 0.825343 | 0.837151 | 0.779817 | 0.057335 | 6.848767 | {'algorithm': 'SAMME', 'learning_rate': 0.3, '... |
| param_15 | AdaBoostClassifier | 0.833933 | 0.846457 | 0.779817 | 0.066640 | 7.872840 | {'algorithm': 'SAMME', 'learning_rate': 0.3, '... |
| param_16 | AdaBoostClassifier | 0.841448 | 0.855404 | 0.791284 | 0.064120 | 7.495873 | {'algorithm': 'SAMME', 'learning_rate': 0.3, '... |
| param_17 | AdaBoostClassifier | 0.820690 | 0.832856 | 0.779817 | 0.053040 | 6.368400 | {'algorithm': 'SAMME', 'learning_rate': 0.5, '... |
| param_18 | AdaBoostClassifier | 0.846463 | 0.857194 | 0.793578 | 0.063616 | 7.421425 | {'algorithm': 'SAMME', 'learning_rate': 0.5, '... |
| param_19 | AdaBoostClassifier | 0.851833 | 0.871868 | 0.800459 | 0.071410 | 8.190408 | {'algorithm': 'SAMME', 'learning_rate': 0.5, '... |
| param_20 | AdaBoostClassifier | 0.851835 | 0.876163 | 0.814220 | 0.061943 | 7.069804 | {'algorithm': 'SAMME', 'learning_rate': 0.5, '... |
| param_21 | AdaBoostClassifier | 0.841813 | 0.855404 | 0.788991 | 0.066414 | 7.764001 | {'algorithm': 'SAMME', 'learning_rate': 0.8, '... |
| param_22 | AdaBoostClassifier | 0.851119 | 0.880816 | 0.805046 | 0.075770 | 8.602269 | {'algorithm': 'SAMME', 'learning_rate': 0.8, '... |
| param_23 | AdaBoostClassifier | 0.856488 | 0.887258 | 0.823394 | 0.063864 | 7.197893 | {'algorithm': 'SAMME', 'learning_rate': 0.8, '... |
| param_24 | AdaBoostClassifier | 0.864361 | 0.891195 | 0.834862 | 0.056333 | 6.321064 | {'algorithm': 'SAMME', 'learning_rate': 0.8, '... |
| param_25 | AdaBoostClassifier | 0.849333 | 0.871868 | 0.798165 | 0.073703 | 8.453473 | {'algorithm': 'SAMME', 'learning_rate': 1.0, '... |
| param_26 | AdaBoostClassifier | 0.856849 | 0.881532 | 0.809633 | 0.071899 | 8.156123 | {'algorithm': 'SAMME', 'learning_rate': 1.0, '... |
| param_27 | AdaBoostClassifier | 0.865799 | 0.887258 | 0.821101 | 0.066157 | 7.456395 | {'algorithm': 'SAMME', 'learning_rate': 1.0, '... |
| param_28 | AdaBoostClassifier | 0.868301 | 0.892985 | 0.830275 | 0.062710 | 7.022485 | {'algorithm': 'SAMME', 'learning_rate': 1.0, '... |
| param_29 | AdaBoostClassifier | 0.690410 | 0.694703 | 0.525229 | 0.169474 | 24.395115 | {'algorithm': 'SAMME.R', 'learning_rate': 0.01... |
| param_30 | AdaBoostClassifier | 0.730494 | 0.735863 | 0.646789 | 0.089074 | 12.104648 | {'algorithm': 'SAMME.R', 'learning_rate': 0.01... |
| param_31 | AdaBoostClassifier | 0.746955 | 0.748389 | 0.649083 | 0.099307 | 13.269407 | {'algorithm': 'SAMME.R', 'learning_rate': 0.01... |
| param_32 | AdaBoostClassifier | 0.762355 | 0.767359 | 0.678899 | 0.088460 | 11.527797 | {'algorithm': 'SAMME.R', 'learning_rate': 0.01... |
| param_33 | AdaBoostClassifier | 0.798500 | 0.809592 | 0.736239 | 0.073353 | 9.060546 | {'algorithm': 'SAMME.R', 'learning_rate': 0.1,... |
| param_34 | AdaBoostClassifier | 0.810310 | 0.818540 | 0.756881 | 0.061659 | 7.532804 | {'algorithm': 'SAMME.R', 'learning_rate': 0.1,... |
| param_35 | AdaBoostClassifier | 0.822836 | 0.836435 | 0.777523 | 0.058912 | 7.043257 | {'algorithm': 'SAMME.R', 'learning_rate': 0.1,... |
| param_36 | AdaBoostClassifier | 0.831426 | 0.850394 | 0.791284 | 0.059109 | 6.950815 | {'algorithm': 'SAMME.R', 'learning_rate': 0.1,... |
| param_37 | AdaBoostClassifier | 0.808879 | 0.823550 | 0.779817 | 0.043734 | 5.310415 | {'algorithm': 'SAMME.R', 'learning_rate': 0.2,... |
| param_38 | AdaBoostClassifier | 0.833933 | 0.851110 | 0.788991 | 0.062119 | 7.298555 | {'algorithm': 'SAMME.R', 'learning_rate': 0.2,... |
| param_39 | AdaBoostClassifier | 0.842884 | 0.866500 | 0.800459 | 0.066041 | 7.621576 | {'algorithm': 'SAMME.R', 'learning_rate': 0.2,... |
| param_40 | AdaBoostClassifier | 0.848615 | 0.876163 | 0.805046 | 0.071117 | 8.116905 | {'algorithm': 'SAMME.R', 'learning_rate': 0.2,... |
| param_41 | AdaBoostClassifier | 0.824985 | 0.843593 | 0.791284 | 0.052309 | 6.200737 | {'algorithm': 'SAMME.R', 'learning_rate': 0.3,... |
| param_42 | AdaBoostClassifier | 0.845748 | 0.862205 | 0.800459 | 0.061746 | 7.161409 | {'algorithm': 'SAMME.R', 'learning_rate': 0.3,... |
| param_43 | AdaBoostClassifier | 0.852911 | 0.878311 | 0.807339 | 0.070971 | 8.080423 | {'algorithm': 'SAMME.R', 'learning_rate': 0.3,... |
| param_44 | AdaBoostClassifier | 0.860068 | 0.886901 | 0.823394 | 0.063506 | 7.160443 | {'algorithm': 'SAMME.R', 'learning_rate': 0.3,... |
| param_45 | AdaBoostClassifier | 0.837870 | 0.862921 | 0.798165 | 0.064755 | 7.504214 | {'algorithm': 'SAMME.R', 'learning_rate': 0.5,... |
| param_46 | AdaBoostClassifier | 0.857207 | 0.885111 | 0.805046 | 0.080065 | 9.045768 | {'algorithm': 'SAMME.R', 'learning_rate': 0.5,... |
| param_47 | AdaBoostClassifier | 0.867228 | 0.892985 | 0.821101 | 0.071884 | 8.049861 | {'algorithm': 'SAMME.R', 'learning_rate': 0.5,... |
| param_48 | AdaBoostClassifier | 0.868300 | 0.897996 | 0.825688 | 0.072308 | 8.052113 | {'algorithm': 'SAMME.R', 'learning_rate': 0.5,... |
| param_49 | AdaBoostClassifier | 0.845753 | 0.881174 | 0.811927 | 0.069247 | 7.858532 | {'algorithm': 'SAMME.R', 'learning_rate': 0.8,... |
| param_50 | AdaBoostClassifier | 0.870089 | 0.896922 | 0.807339 | 0.089583 | 9.987772 | {'algorithm': 'SAMME.R', 'learning_rate': 0.8,... |
| param_51 | AdaBoostClassifier | 0.870450 | 0.906228 | 0.823394 | 0.082833 | 9.140434 | {'algorithm': 'SAMME.R', 'learning_rate': 0.8,... |
| param_52 | AdaBoostClassifier | 0.870808 | 0.912312 | 0.830275 | 0.082037 | 8.992193 | {'algorithm': 'SAMME.R', 'learning_rate': 0.8,... |
| param_53 | AdaBoostClassifier | 0.861142 | 0.879742 | 0.816514 | 0.063229 | 7.187166 | {'algorithm': 'SAMME.R', 'learning_rate': 1.0,... |
| param_54 | AdaBoostClassifier | 0.870093 | 0.900859 | 0.811927 | 0.088932 | 9.871953 | {'algorithm': 'SAMME.R', 'learning_rate': 1.0,... |
| param_55 | AdaBoostClassifier | 0.872244 | 0.910523 | 0.834862 | 0.075660 | 8.309532 | {'algorithm': 'SAMME.R', 'learning_rate': 1.0,... |
| param_56 | AdaBoostClassifier | 0.871889 | 0.918397 | 0.850917 | 0.067479 | 7.347494 | {'algorithm': 'SAMME.R', 'learning_rate': 1.0,... |
# filter parameter - accuracy
filter = (adb_tuning_accuracy['CV Accuracy'] > 0.8) & (adb_tuning_accuracy['Diff'] > 0)
adb_tuning_accuracy[filter].sort_values(by=['CV Accuracy','Diff'], ascending=[False,True]).nlargest(3, columns=['CV Accuracy'])
| Model | CV Accuracy | Accuracy_Train | Accuracy_Test | Diff | Diff (%) | Parameters | |
|---|---|---|---|---|---|---|---|
| Parameter Set | |||||||
| param_55 | AdaBoostClassifier | 0.872244 | 0.910523 | 0.834862 | 0.075660 | 8.309532 | {'algorithm': 'SAMME.R', 'learning_rate': 1.0,... |
| param_56 | AdaBoostClassifier | 0.871889 | 0.918397 | 0.850917 | 0.067479 | 7.347494 | {'algorithm': 'SAMME.R', 'learning_rate': 1.0,... |
| param_52 | AdaBoostClassifier | 0.870808 | 0.912312 | 0.830275 | 0.082037 | 8.992193 | {'algorithm': 'SAMME.R', 'learning_rate': 0.8,... |
# get parameter
params_adb_accuracy = adb_tuning_accuracy.loc['param_56']
params_adb_accuracy['Parameters']
"{'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 200}"
from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay
params = {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 200}
adaboost_model = AdaBoostClassifier(**params, random_state=42)
adaboost_model.fit(X_train, y_train)
y_pred = adaboost_model.predict(X_test)
y_pred_train = adaboost_model.predict(X_train)
print('Classification Report on Test Data')
print(classification_report(y_test, y_pred))
print('\n')
print('Classification Report on Train Data')
print(classification_report(y_train, y_pred_train))
Classification Report on Test Data
precision recall f1-score support
0 0.90 0.92 0.91 367
1 0.53 0.46 0.50 69
accuracy 0.85 436
macro avg 0.72 0.69 0.70 436
weighted avg 0.84 0.85 0.85 436
Classification Report on Train Data
precision recall f1-score support
0 0.91 0.93 0.92 1397
1 0.93 0.91 0.92 1397
accuracy 0.92 2794
macro avg 0.92 0.92 0.92 2794
weighted avg 0.92 0.92 0.92 2794
from sklearn.utils.class_weight import compute_sample_weight
from sklearn.metrics import confusion_matrix
# Compute confusion matrix
cm = confusion_matrix(y_test, y_pred)
tn, fp, fn, tp = cm.ravel()
# Define names for confusion matrix elements
names = ['True Negative', 'False Positive', 'False Negative', 'True Positive']
# Convert counts to string format
counts = ['{0:0.0f}'.format(value) for value in cm.flatten()]
# Create labels with counts and percentages
labels = [f'{v1}\n\n{v2}' for v1, v2 in zip(names, counts)]
labels = np.asarray(labels).reshape(2, 2)
# Create heatmap with annotations
heatmap = sns.heatmap(cm, annot=labels, annot_kws={'size': 13}, fmt='', cmap='coolwarm', center=100)
# Customize tick labels and rotations
heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=13)
heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=0, ha='right', fontsize=13)
# Set title, ylabel, and xlabel
plt.title(f'Confusion Matrix\nPrecision', fontsize=13, color='black')
plt.ylabel('Actual Label', fontsize=13)
plt.xlabel('\nPredicted Label', fontsize=13)
# Show the plot
plt.show()
# menggunakan data awal
cost = 3 # asumsi cost per customer ($3)
revenue = 11 # asumsi revenue per customer ($11)
total_customer = 2240
total_response = 334
rate_accept = total_response/total_customer * 100
total_cost = total_customer * cost
total_revenue = total_response * revenue
total_profit = total_revenue - total_cost
profit_margin = (total_profit/total_revenue) * 100
print(f'total customer: {total_customer}')
print(f'total response: {total_response}')
print(f'rate accept: {rate_accept:.2f}%')
print(f'total cost: {total_cost}')
print(f'total revenue: {total_revenue}')
print(f'total profit: {total_profit}')
print(f'profit margin before pemodelan: {profit_margin:.2f}%')
total customer: 2240 total response: 334 rate accept: 14.91% total cost: 6720 total revenue: 3674 total profit: -3046 profit margin before pemodelan: -82.91%
# menggunakan data test - tanpa sample weight
cost = 3 # asumsi cost per customer ($3)
revenue = 11 # asumsi revenue per customer ($11)
total_customer = tp + fp
total_response = tp
rate_accept = total_response/total_customer * 100
total_cost = total_customer * cost
total_revenue = total_response * revenue
total_profit = total_revenue - total_cost
profit_margin = ((total_profit)/total_revenue) * 100
print(f'total customer: {total_customer}')
print(f'total response: {total_response}')
print(f'rate accept: {rate_accept:.2f}%')
print(f'total cost: {total_cost}')
print(f'total revenue: {total_revenue}')
print(f'total profit: {total_profit}')
print(f'profit margin after pemodelan: {profit_margin:.2f}%')
total customer: 60 total response: 32 rate accept: 53.33% total cost: 180 total revenue: 352 total profit: 172 profit margin after pemodelan: 48.86%
Pada table confusion matrix terdapat nilai sample response dari customer true positive sebanyak 32 dan false positive 28 customer .
Sebelum dilakukan modeling presentase response rate berada di angka 14.91% setelah dilakukan modeling response rate meningkat menjadi 53.33% ada peningkatan sebesar 38%.
Presentase Profit margin sebelum dilakukan pemodelan sebesar -82.91% setelah dilakukan pemodelan menggunakan data test presentase profit margin naik menjadi 48.86%.
Setelah dilakukan modeling cost campaign menjadi lebih efisien dikarenakan lebih targeted pada segment customer tertentu menjadi 180 dollar dari total customer yang benar benar meresponse campaign (true positive)dengan asumsi cost yang dikeluarkan 3 dollar untuk setiap customer dan revenue yang didapatkan $11/ customer.
# get clean data from data preprocessing based on selected features in modelling
data_train = pd.merge(data_before_transform_train[selected_features], y_train, left_index=True, right_index=True)
data_test = pd.merge(data_before_transform_test[selected_features], y_test, left_index=True, right_index=True)
data_clean = pd.concat([data_train,data_test])
data_clean['RFM_Cat'] = oe_rfm.inverse_transform(data_clean[['RFM_Cat']])
data_clean['Education'] = oe_edu.inverse_transform(data_clean[['Education']])
data_clean.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 2084 entries, 0 to 435 Data columns (total 16 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 AcceptedCmp1 2084 non-null int64 1 AcceptedCmp2 2084 non-null int64 2 AcceptedCmp3 2084 non-null int64 3 AcceptedCmp4 2084 non-null int64 4 AcceptedCmp5 2084 non-null int64 5 Customer_Lifespan 2084 non-null int64 6 Education 2084 non-null object 7 MntFishProducts 2084 non-null int64 8 MntFruits 2084 non-null int64 9 MntGoldProds 2084 non-null int64 10 MntSweetProducts 2084 non-null int64 11 NumCatalogPurchases 2084 non-null int64 12 NumWebPurchases 2084 non-null int64 13 RFM_Cat 2084 non-null object 14 Recency 2084 non-null int64 15 Response 2084 non-null int64 dtypes: int64(14), object(2) memory usage: 276.8+ KB
data = data_clean.copy()
data.head()
| AcceptedCmp1 | AcceptedCmp2 | AcceptedCmp3 | AcceptedCmp4 | AcceptedCmp5 | Customer_Lifespan | Education | MntFishProducts | MntFruits | MntGoldProds | MntSweetProducts | NumCatalogPurchases | NumWebPurchases | RFM_Cat | Recency | Response | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 1 | 0 | 0 | 347 | Graduation | 12 | 8 | 13 | 2 | 0 | 2 | at risk cust | 49 | 0 |
| 1 | 0 | 0 | 0 | 0 | 0 | 525 | Master | 151 | 15 | 15 | 38 | 3 | 5 | loyal cust | 30 | 0 |
| 2 | 0 | 0 | 0 | 1 | 0 | 623 | PhD | 0 | 0 | 10 | 0 | 4 | 3 | loyal cust | 59 | 0 |
| 3 | 0 | 0 | 0 | 0 | 0 | 418 | PhD | 0 | 0 | 2 | 0 | 0 | 1 | at risk cust | 43 | 0 |
| 4 | 0 | 0 | 0 | 0 | 0 | 269 | Graduation | 2 | 4 | 321 | 4 | 0 | 25 | loyal cust | 0 | 0 |
# 10 feature importance based on adaboost model (hyper parameter tuning)
importance_adb.nlargest(10)
NumWebPurchases 0.235 Education 0.175 RFM_Cat 0.125 NumCatalogPurchases 0.100 MntFishProducts 0.070 MntGoldProds 0.070 MntSweetProducts 0.055 Customer_Lifespan 0.050 Recency 0.050 MntFruits 0.030 dtype: float64
# Menghitung frekuensi kategori dan mengurutkannya dari besar ke kecil
rfm_counts = data['RFM_Cat'].value_counts().sort_values(ascending=False)
# Membuat subplot
fig, axes = plt.subplots(1, 2, figsize=(12, 6))
# Plot countplot tanpa bingkai
sns.countplot(x=data['RFM_Cat'], order=rfm_counts.index,
ax=axes[0], palette=sns.color_palette())
axes[0].set_title('Proportions of Customer Segmentation')
# Menambahkan nilai label pada countplot
for p in axes[0].patches:
axes[0].annotate(f'{p.get_height():0.0f}', (p.get_x() + p.get_width() / 2., p.get_height()),
ha='center', va='center', xytext=(0, 5), textcoords='offset points')
# Plot pie plot
axes[1].pie(rfm_counts, labels=rfm_counts.index,
autopct='%1.0f%%', startangle=140, textprops={'fontsize': 12, 'color': 'black'})
axes[1].set_title('Percentages of Customer Segmentation')
plt.tight_layout()
plt.show()
/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. and should_run_async(code)
# Menghitung frekuensi kategori dan mengurutkannya dari besar ke kecil
education_counts = data['Education'].value_counts().sort_values(ascending=False)
fig, axes = plt.subplots(1, 2, figsize=(12, 6))
sns.countplot(x=data['Education'], order=education_counts.index, ax=axes[0], palette=sns.color_palette())
axes[0].set_title('Proportions of Customer Education Level')
for p in axes[0].patches:
axes[0].annotate(f'{p.get_height():0.0f}', (p.get_x() + p.get_width() / 2., p.get_height()),
ha='center', va='center', xytext=(0, 5), textcoords='offset points')
# Plot pie plot
axes[1].pie(education_counts, labels=education_counts.index, autopct='%1.0f%%', startangle=140, textprops={'fontsize': 12, 'color': 'black'})
axes[1].set_title('Percentages of Customer Education Level')
plt.tight_layout()
plt.show()
/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. and should_run_async(code)
# 'Education'
education_counts = data.groupby('Education')['Response'].value_counts(normalize=True).unstack()
colors = ['#e41a1c', '#377eb8']
fig, axes = plt.subplots(1, 2, figsize=(12, 6))
education_counts.plot(kind='bar', stacked=True, color=colors, ax=axes[0])
axes[0].set_title('Percentage of Response by Education')
axes[0].set_ylabel('Percentage')
axes[0].set_xticklabels(education_counts.index, rotation=0, ha='center')
handles, labels = axes[0].get_legend_handles_labels()
axes[0].legend(reversed(handles), reversed(labels), bbox_to_anchor=(
1.2, 1), loc='upper right', title='Response')
for p in axes[0].patches:
if p.get_height() >= 0.5:
vertical_position = 'bottom' if p.get_height() < 0.5 else 'top'
axes[0].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., p.get_height()),
ha='center', va=vertical_position, xytext=(0, 10 if p.get_height() < 0.5 else -10),
fontsize=12, color='white',
textcoords='offset points')
for p in axes[0].patches:
if p.get_height() < 0.5:
axes[0].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., 1),
ha='center', va='top', xytext=(0, -1),
fontsize=12, color='white',
textcoords='offset points')
# 'RFM_Cat'
rfm_counts = data.groupby('RFM_Cat')['Response'].value_counts(normalize=True).unstack()
rfm_counts.plot(kind='bar', stacked=True, color=colors, ax=axes[1])
axes[1].set_title('Percentage of Response by RFM_Cat')
axes[1].set_ylabel('Percentage')
axes[1].set_xticklabels(rfm_counts.index, rotation=0, ha='center')
handles, labels = axes[1].get_legend_handles_labels()
axes[1].legend(reversed(handles), reversed(labels), bbox_to_anchor=(
1.2, 1), loc='upper right', title='Response')
for p in axes[1].patches:
if p.get_height() >= 0.5:
vertical_position = 'bottom' if p.get_height() < 0.5 else 'top'
axes[1].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., p.get_height()),
ha='center', va=vertical_position, xytext=(0, 10 if p.get_height() < 0.5 else -10),
fontsize=12, color='white',
textcoords='offset points')
for p in axes[1].patches:
if p.get_height() <= 0.5:
axes[1].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., 1),
ha='center', va='top', xytext=(0, -1),
fontsize=12, color='white',
textcoords='offset points')
plt.tight_layout()
plt.show()
# graduation education level
filter_graduation = (data['Education'] == 'Graduation') & (data['RFM_Cat'] == 'loyal cust') & (data['Response'] == 1)
filtered_data_graduation = data[filter_graduation][['Customer_Lifespan', 'Recency', 'NumCatalogPurchases', 'NumWebPurchases',
'MntFishProducts', 'MntFruits', 'MntGoldProds', 'MntSweetProducts']]
from scipy.stats import t
confidence_level = 0.95
# Calculate confidence interval for each variable
print("Confidence Interval Mean of Customer in Graduation Education Level")
for column, values in filtered_data_graduation.items():
sample_mean = np.mean(values)
sample_std = np.std(values)
degrees_of_freedom = len(values) - 1
alpha = 1 - confidence_level
t_value = t.ppf(1 - alpha / 2, df=degrees_of_freedom)
margin_of_error = t_value * (sample_std / np.sqrt(len(values)))
lower_bound = sample_mean - margin_of_error
upper_bound = sample_mean + margin_of_error
print(
f"Confidence Interval for {column}: ({lower_bound:.2f}, {upper_bound:.2f})")
Confidence Interval Mean of Customer in Graduation Education Level Confidence Interval for Customer_Lifespan: (393.45, 494.46) Confidence Interval for Recency: (32.72, 46.01) Confidence Interval for NumCatalogPurchases: (4.07, 5.64) Confidence Interval for NumWebPurchases: (4.77, 5.67) Confidence Interval for MntFishProducts: (53.12, 86.67) Confidence Interval for MntFruits: (39.56, 63.70) Confidence Interval for MntGoldProds: (60.24, 92.58) Confidence Interval for MntSweetProducts: (34.38, 58.68)
# filter apriori
filter_apriori_graduation = (data['Education'] == 'Graduation') & (data['RFM_Cat'] == 'loyal cust') & (data['Response'] == 1)
# web - apriori
web_selling_graduation = data[filter_apriori_graduation][['NumWebPurchases', 'MntFishProducts', 'MntFruits', 'MntGoldProds', 'MntSweetProducts']]
# catalog - aprioroi
catalog_selling_graduation = data[filter_apriori_graduation][['NumCatalogPurchases', 'MntFishProducts', 'MntFruits', 'MntGoldProds', 'MntSweetProducts']]
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
# Encode data dalam format one-hot encoding
web_encoded_graduation = web_selling_graduation.astype(bool).astype(int)
catalog_encoded_graduation = catalog_selling_graduation.astype(bool).astype(int)
# Terapkan algoritma Apriori untuk menemukan item-item yang sering muncul bersama-sama
frequent_itemsets_web_graduation = apriori(web_encoded_graduation, min_support=0.1, use_colnames=True)
frequent_itemsets_catalog_graduation = apriori(catalog_encoded_graduation, min_support=0.1, use_colnames=True)
# Temukan association rules
rules_web_graduation = association_rules(frequent_itemsets_web_graduation, metric="lift", min_threshold=1)
rules_catalog_graduation = association_rules(frequent_itemsets_catalog_graduation, metric="lift", min_threshold=1)
/usr/local/lib/python3.10/dist-packages/mlxtend/frequent_patterns/fpcommon.py:110: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type warnings.warn( /usr/local/lib/python3.10/dist-packages/mlxtend/frequent_patterns/fpcommon.py:110: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type warnings.warn(
pd.set_option('display.max_colwidth', 100)
frequent_itemsets_web_graduation[frequent_itemsets_web_graduation['itemsets'].apply(lambda x: 'NumWebPurchases' in x)]
/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. and should_run_async(code)
| support | itemsets | |
|---|---|---|
| 0 | 1.000000 | (NumWebPurchases) |
| 5 | 0.897059 | (NumWebPurchases, MntFishProducts) |
| 6 | 0.926471 | (MntFruits, NumWebPurchases) |
| 7 | 0.955882 | (NumWebPurchases, MntGoldProds) |
| 8 | 0.823529 | (NumWebPurchases, MntSweetProducts) |
| 15 | 0.852941 | (MntFruits, NumWebPurchases, MntFishProducts) |
| 16 | 0.882353 | (MntGoldProds, NumWebPurchases, MntFishProducts) |
| 17 | 0.794118 | (MntSweetProducts, NumWebPurchases, MntFishProducts) |
| 18 | 0.882353 | (MntFruits, NumWebPurchases, MntGoldProds) |
| 19 | 0.794118 | (MntFruits, NumWebPurchases, MntSweetProducts) |
| 20 | 0.808824 | (MntSweetProducts, NumWebPurchases, MntGoldProds) |
| 25 | 0.838235 | (MntGoldProds, MntFruits, NumWebPurchases, MntFishProducts) |
| 26 | 0.764706 | (MntFruits, MntSweetProducts, NumWebPurchases, MntFishProducts) |
| 27 | 0.779412 | (MntGoldProds, MntSweetProducts, NumWebPurchases, MntFishProducts) |
| 28 | 0.779412 | (MntFruits, MntSweetProducts, NumWebPurchases, MntGoldProds) |
| 30 | 0.750000 | (MntSweetProducts, NumWebPurchases, MntFishProducts, MntFruits, MntGoldProds) |
pd.set_option('display.max_colwidth', 100)
frequent_itemsets_catalog_graduation[frequent_itemsets_catalog_graduation['itemsets'].apply(lambda x: 'NumCatalogPurchases' in x)]
/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. and should_run_async(code)
| support | itemsets | |
|---|---|---|
| 0 | 1.000000 | (NumCatalogPurchases) |
| 5 | 0.897059 | (NumCatalogPurchases, MntFishProducts) |
| 6 | 0.926471 | (MntFruits, NumCatalogPurchases) |
| 7 | 0.955882 | (NumCatalogPurchases, MntGoldProds) |
| 8 | 0.823529 | (NumCatalogPurchases, MntSweetProducts) |
| 15 | 0.852941 | (MntFruits, NumCatalogPurchases, MntFishProducts) |
| 16 | 0.882353 | (MntGoldProds, NumCatalogPurchases, MntFishProducts) |
| 17 | 0.794118 | (NumCatalogPurchases, MntSweetProducts, MntFishProducts) |
| 18 | 0.882353 | (MntFruits, NumCatalogPurchases, MntGoldProds) |
| 19 | 0.794118 | (MntFruits, NumCatalogPurchases, MntSweetProducts) |
| 20 | 0.808824 | (NumCatalogPurchases, MntSweetProducts, MntGoldProds) |
| 25 | 0.838235 | (MntGoldProds, MntFruits, NumCatalogPurchases, MntFishProducts) |
| 26 | 0.764706 | (MntFruits, NumCatalogPurchases, MntSweetProducts, MntFishProducts) |
| 27 | 0.779412 | (MntGoldProds, NumCatalogPurchases, MntSweetProducts, MntFishProducts) |
| 28 | 0.779412 | (MntFruits, NumCatalogPurchases, MntSweetProducts, MntGoldProds) |
| 30 | 0.750000 | (MntSweetProducts, NumCatalogPurchases, MntFishProducts, MntFruits, MntGoldProds) |
Dilakukan perhitungan confidence interval dengan tingkat kepercayaan 95% dan nilai rata-rata dari cutomer dengan tingkat pendidikan graduation. Confidence interval memberikan gambaran tentang seberapa pasti kita dengan nilai rata-rata yang diestimasi dari sampel data. Berikut insight yang didapatkan :
Rata-rata periode sejak pembelian terakhir (Recency) adalah 32.72 hingga 46.01 hari. Ini menunjukkan bahwa sebagian pelanggan cukup aktif melakukan pembelian ulang.
Dapat disimpulkan bahwa dengan tingkat kepercayaan 95%, kita dapat yakin bahwa rata-rata rentang lifespan customer graduation berada di antara 393.45 dan 494.46 hari.
Pada hasil algoritma apriori melalui chanel pembelanjaan website dan catalogue dapat dilihat keterkaitan dan frekuensi untuk item yang dibeli secara bersamaan, fish product menjadi item yang sering dibeli dengan nilai support 89%. Dan ada beberapa item yang dibeli secara bersamaan melalui chanel penjualan.
Customer yang melakukan pembelian melalui chanel website dan catalogue cenderung juga membeli produk tertentu seperti MntFishProducts, MntFruits, MntGoldProds, dan MntSweetProducts
Informasi dari output apriori pada chanel pembelian bisa digunakan untuk placement produk apa saja yang akan di jual di website dan catalogue membuat paket penawaran yang mencakup item-item yang cenderung dibeli secara bersamaan (bundling).
Dengan strategi ini, perusahaan dapat meningkatkan penjualan lintas produk dan memberikan pengalaman berbelanja yang lebih terpersonalisasi kepada pelanggan.